Search results for: emotion recognition

Authors: Junwen Chen, Celina Feleppa, Tingyue Sun, Satoko Sasagawa, Michael Smithson

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School refusal behaviours refer to behaviours to avoid school attendance, chronic lateness in arriving at school, or regular early dismissal. Poor attendance in schools is highly correlated with anxiety, depression, suicide attempts, delinquency, violence, and substance use and abuse. Poor attendance is also a strong indicator of lower achievement in school, as well as problematic social-emotional development. Long-term consequences of school refusal behaviours include fewer opportunities for higher education, employment, and social difficulties, and high risks of later psychiatric illness. Given its negative impacts on youth educational outcomes and well-being, a thorough understanding of factors that are involved in the development of this phenomenon is warranted for developing effective management approaches. This study investigated parental and adolescent factors that may contribute to school refusal behaviours by specifically focusing on the role of parental and adolescents’ anxiety and depression, emotion dysregulation, and parental rearing style. Findings are expected to inform the identification of both parental and adolescents’ factors that may contribute to school refusal behaviours. This knowledge will enable novel and effective approaches that incorporate these factors to managing school refusal behaviours in adolescents, which in turn improve their school and daily functioning. Results are important for an integrative understanding of school refusal behaviours. Furthermore, findings will also provide information for policymakers to weigh the benefits of interventions targeting school refusal behaviours in adolescents. One-hundred-and-six adolescents aged 12-18 years (mean age = 14.79 years old, SD = 1.78, males = 44) and their parents (mean age = 47.49 years old, SD = 5.61, males = 27) completed an online questionnaire measuring both parental and adolescents’ anxiety, depression, emotion dysregulation, parental rearing styles, and adolescents’ school refusal behaviours. Adolescents with school refusal behaviours reported greater anxiety and depression, with their parents showing greater emotion dysregulation. Parental emotion dysregulation and adolescents’ anxiety and depression predicted school refusal behaviours independently. To date, only limited studies have investigated the interplay between parental and youth factors in relation to youth school refusal behaviours. Although parental emotion dysregulation has been investigated in relation to youth emotion dysregulation, little is known about its role in the context of school refusal. This study is one of the very few that investigated both parental and adolescent factors in relation to school refusal behaviours in adolescents. The findings support the theoretical models that emphasise the role of youth and parental psychopathology in school refusal behaviours. Future management of school refusal behaviours should target adolescents’ anxiety and depression while incorporating training for parental emotion regulation skills.

Keywords: adolescents, school refusal behaviors, parental factors, anxiety and depression, emotion dysregulation

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Authors: João Rato, Nuno Costa

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The human verbal communication is a two-way process which requires a mutual understanding that will result in some considerations. This kind of communication, also called dialogue, besides the supposed human agents it can also be performed between human agents and machines. The interaction between Men and Machines, by means of a natural language, has an important role concerning the improvement of the communication between each other. Aiming at knowing the performance of some speech recognition systems, this document shows the results of the accomplished tests according to the Word Error Rate evaluation method. Besides that, it is also given a set of information linked to the systems of Man-Machine communication. After this work has been made, conclusions were drawn regarding the Speech Recognition Systems, among which it can be mentioned their poor performance concerning the voice interpretation in noisy environments.

Keywords: automatic speech recognition, man-machine conversation, speech recognition, spoken dialogue systems, word error rate

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Sabahat Nadeem, Farman Ali Khan

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Emotions are considered as a vital factor affecting the process of information handling, level of attention, memory capacity and decision making. Latest e-Learning systems are therefore taking into consideration the effective state of learners to make the learning process more effective and enjoyable. One such use of user’s affective information is in the systems that tend to regulate users’ emotions to a state optimally desirable for learning. So for, this objective has been tried to be achieved with the help of teaching strategies, background music, guided imagery, video clips and odors. Nevertheless, we know that colors can affect human emotions. Relationship between color and emotions has a strong influence on how we perceive our environment. Similarly, the colors of the interface can also affect the user positively as well as negatively. This affective behavior of color and its use as emotion regulation agent is not yet exploited. Therefore, this research proposes a Color-based Emotion Regulation Model (CERM), a new framework that can automatically adapt its colors according to user’s emotional state and her personality type and can help in producing a desirable emotional effect, aiming at providing an unobtrusive emotional support to the users of e-learning environment. The evaluation of CERM is carried out by comparing it with classical non-adaptive, static colored learning management system. Results indicate that colors of the interface, when carefully selected has significant positive impact on learner’s emotions.

Keywords: effective learning, e-learning, emotion regulation, emotional design

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Authors: Dorottya Horváth, Andras Lang, Diana Varro-Horvath

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This research activity and study is part of a major research effort to gain an integrative, neuropsychological, and personality psychological understanding of Attention Deficit Hyperactivity Disorder (ADHD) and thus improve the specification of diagnostic and therapeutic care. In the past, the neuropsychology section has investigated working memory, executive function, attention, and behavioural manifestations in children. Currently, we are looking for personality psychological protective factors for ADHD and its symptomatic exacerbation. We hypothesise that secure attachment, adaptive emotion regulation, and high resilience are protective factors. The aim of this study is to measure and report the results of a Hungarian sample of the Cognitive Emotion Regulation Questionnaire for Children (CERQ-k) because before studying groups with different developmental differences, it is essential to know the average scores of groups with neurotypical devel-opment. Until now, there was no Hungarian version of the above test, so we used our own translation. This questionnaire has been developed to assess children's thoughts after experiencing negative life events. It consists of 4-4 items per subscale, for a total of 36 items. The response categories for each item range from 1 (almost never) to 5 (almost always). The subscales were self-blame, blaming others, acceptance, planning, positive refocusing, rumination or thought-focusing, positive reappraisal, putting into perspective, and catastrophizing. The data for this study were collected from 120 children aged 9-14 years. It was analysed using descriptive statistical analysis, where the mean and standard deviation values for each age group, as well as the Cronbach's alpha value, were significant in testing the reliability of the questionnaire. The results showed that the questionnaire is a reliable and valid measuring instrument also on a Hungarian sample. These developments and results will allow the use of a version of the Cognitive Emotion Regulation Questionnaire for children in Hungarian and pave the way for the study of different developmental groups such as children with learning disabilities and/or with ADHD.

Keywords: neurotypical development, emotion regulation, negative life events, CERQ-k, Hungarian average scores

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Marcio Leal, Marta Villamil

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Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%.

Keywords: artificial neural network, computer vision, dynamic time warping, infrared, sign language recognition

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Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Rajpal Kaur, Pooja Choudhary

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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

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This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. In addition, in existing studies, deep acting and surface acting are highly related to a positive outcome variable and a negative outcome variable, respectively. It was confirmed that for employees performing emotional labor, deep acting and surface acting are highly related to OCB and CWB, respectively. While positive emotion that employees come to experience during job performance process can easily trigger a positive non-task behavior such as OCB, negative emotion that employees experience through excessive workload or unfair treatment can easily induce a negative behavior like CWB. The two management behaviors of emotional labor, surface acting and deep acting, can have either a positive or negative effect on non-task behavior of employees, depending on which one they would choose. Thus, the purpose of this review paper is to clarify the relationship between emotional labor and non-task behavior more specifically.

Keywords: emotion labor, non-task behavior, OCB, CWB

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Authors: Mohamed Lotfy, Ghada Soliman

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Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Sakshi Ghai

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The word ‘emotion’ has been microscopically studied through psychological, anthropological and biological lenses and have indubitably been one of the most researched concepts as, in all situations and reactions that constitute human life, emotions form the very niche of our mutual existence. While understanding the social aspects of cognition, one can realize that emotions are deeply interwoven with language and thereby are pivotal in inducing human actions and behavior. The society or the outward social structure is the result of the inward psychological structure of our human relationships, for the individual is the result of the total experience, knowledge and conduct of man. The aim of this paper is threefold: first, to establish the relation between mental representations of emotions and its neuropsychological connection with language on a conscious and sub-conscious level; secondly, to describe how innate, basic and higher cognitive emotions affect the constantly changing state of an agent and peruse its assistance in determining the moral compass within all beings. Lastly, in the course of this paper, the concept of the architecture of mind is explored considering how it has developed an ability to display adaptive emotional states and responses, which are in sync with the language of thought. For every response to the social environment is so deeply determined by the very social milieu in which one is situated, language has a fundamental role in constructing emotions and articulating behavior. Being linguistic beings, we tend to associate emotion, feelings and other aspects of inwards mental states intrinsically with the language we use. This paper aims to devise a discursive approach to understand how emotions are fabricated, intertwined with the mental constructs further expressed and communicated through the various units of language.

Keywords: mental representation, emotion, language, psychology

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Authors: Natalia Alonso-Alberca, Ana I. Vergara

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From the very first years of their life, children are confronted with situations in which they need to deal with emotions. The family provides the first emotional experiences, and it is in the family context that children usually take their first steps towards acquiring emotion knowledge. Parents play a key role in this important task, helping their children develop emotional skills that they will need in challenging situations throughout their lives. Specifically, mothers are models imitated by their children. They create specific spatial and temporal contexts in which children learn about emotions, their causes, consequences, and complexity. This occurs not only through what mothers say or do directly to the child. Rather, it occurs, to a large extent, through the example that they set using their own emotional skills. The aim of the current study was to analyze how maternal abilities to perceive and to manage emotions influence children’s emotion knowledge, specifically, their emotion situation knowledge, taking into account the role played by the mother’s personality, the time spent together, and controlling the effect of age, sex and the child’s verbal abilities. Participants were 153 children from 4 schools in Spain, and their mothers. Children (41.8% girls)age range was 35 - 72 months. Mothers (N = 140) age (M = 38.7; R = 27-49). Twelve mothers had more than one child participating in the study. Main variables were the child´s emotion situation knowledge (ESK), measured by the Emotion Matching Task (EMT), and receptive language, using the Picture Vocabulary Test. Also, their mothers´ Emotional Intelligence (EI), through the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT) and personality, with The Big Five Inventory were analyzed. The results showed that the predictive power of maternal emotional skills on ESK was moderated by the mother’s personality, affecting both the direction and size of the relationships detected: low neuroticism and low openness to experience lead to a positive influence of maternal EI on children’s ESK, while high levels in these personality dimensions resulted in a negative influence on child´s ESK. The time that the mother and the child spend together was revealed as a positive predictor of this EK, while it did not moderate the influence of the mother's EI on child’s ESK. In light of the results, we can infer that maternal EI is linked to children’s emotional skills, though high level of maternal EI does not necessarily predict a greater degree of emotionknowledge in children, which seems rather to depend on specific personality profiles. The results of the current study indicate that a good level of maternal EI does not guarantee that children will learn the emotional skills that foster prosocial adaptation. Rather, EI must be accompanied by certain psychological characteristics (personality traits in this case).

Keywords: emotional intelligence, emotion situation knowledge, mothers, personality, young children

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Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

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Authors: M. Jebali, M. Jemni

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This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm such as an input to another system which attempt to bring the HCI performance nearby the human-human interaction, by modeling an intelligent sign language recognition system based on prediction in the context of dialogue between the system (avatar) and the interlocutor. For the purpose of hand segmentation, an overcoming occlusion approach has been proposed for superior results for detection of hand from an image.

Keywords: HCI, sign language recognition, object tracking, hand segmentation

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Authors: S. Jagadeesh Kumar

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Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: Elyria Kemp, Kelly Cowart, My Bui

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According to the National Institute of Mental Health, an estimated 31.9% of adolescents have had an anxiety disorder. Several environmental factors may help to contribute to high levels of anxiety and depression in young people (i.e., Generation Z, Millennials). However, as young people negotiate life on social media, they may begin to evaluate themselves using excessively high standards and adopt self-perfectionism tendencies. Broadly defined, self-perfectionism involves very critical evaluations of the self. Perfectionism may also come from others and may manifest as socially prescribed perfectionism, and young adults are reporting higher levels of socially prescribed perfectionism than previous generations. This rising perfectionism is also associated with anxiety, greater physiological reactivity, and a sense of social disconnection. However, theories from psychology suggest that improvement in emotion regulation can contribute to enhanced psychological and emotional well-being. Emotion regulation refers to the ways people manage how and when they experience and express their emotions. Cognitive reappraisal and expressive suppression are common emotion regulation strategies. Cognitive reappraisal involves changing the meaning of a stimulus that involves construing a potentially emotion-eliciting situation in a way that changes its emotional impact. By contrast, expressive suppression involves inhibiting the behavioral expression of emotion. The purpose of this research is to examine the efficacy of social marketing initiatives which promote emotion regulation strategies to help young adults regulate their emotions. In Study 1 a single factor (emotional regulation strategy: a cognitive reappraisal, expressive, control) between-subjects design was conducted using an online, non-student consumer panel (n=96). Sixty-eight percent of participants were male, and 32% were female. Study participants belonged to the Millennial and Gen Z cohort, ranging in age from 22 to 35 (M=27). Participants were first told to spend at least three minutes writing about a public speaking appearance which made them anxious. The purpose of this exercise was to induce anxiety. Next, participants viewed one of three advertisements (randomly assigned) which promoted an emotion regulation strategy—cognitive reappraisal, expressive suppression, or an advertisement non-emotional in nature. After being exposed to one of the ads, participants responded to a measure composed of two items to access their emotional state and the efficacy of the messages in fostering emotion management. Findings indicated that individuals in the cognitive reappraisal condition (M=3.91) exhibited the most positive feelings and more effective emotion regulation than the expressive suppression (M=3.39) and control conditions (M=3.72, F(1,92) = 3.3, p<.05). Results from this research can be used by institutions (e.g., schools) in taking a leadership role in attacking anxiety and other mental health issues. Social stigmas regarding mental health can be removed and a more proactive stance can be taken in promoting healthy coping behaviors and strategies to manage negative emotions.

Keywords: emotion regulation, anxiety, social marketing, generation z

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Authors: Yao-Hong Tsai

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This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: authentication, iris recognition, adaboost, local binary pattern

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Authors: Min-Ha Hong, Kee-Won Kim, Seung-Hoon Kim

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The character in the story-based content, such as novels and movies, is one of the core elements to understand the story. In particular, the character’s tendency is an important factor to analyze the story-based content, because it has a significant influence on the storyline. If readers have the knowledge of the tendency of characters before reading a novel, it will be helpful to understand the structure of conflict, episode and relationship between characters in the novel. It may therefore help readers to select novel that the reader wants to read. In this paper, we propose a method of extracting the tendency of the characters from a novel written in Korean. In advance, we build the dictionary with pairs of the emotional words in Korean and English since the emotion words in the novel’s sentences express character’s feelings. We rate the degree of polarity (positive or negative) of words in our emotional words dictionary based on SenticNet. Then we extract characters and emotion words from sentences in a novel. Since the polarity of a word grows strong or weak due to sentence features such as quotations and modifiers, our proposed method consider them to calculate the polarity of characters. The information of the extracted character’s polarity can be used in the book search service or book recommendation service.

Keywords: character tendency, data mining, emotion word, Korean novel

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Authors: Joanna Maj

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Awe inspiring is the idea that emotions could be present in human-machine interactions, both on the human side as well as the machine side. Human factors present intriguing components and are examined in detail while discussing this controversial topic. Mood, attention, memory, performance, assessment, causes of emotion, and neurological responses are analyzed as components of the interaction. Problems in computer-based technology, revenge of the system on its users and design, and applications comprise a major part of all descriptions and examples throughout this paper. It also allows for critical thinking while challenging intriguing questions regarding future directions in research, dealing with emotion in human-machine interactions.

Keywords: biocomputing, biomedical engineering, emotions, human-machine interaction, interfaces

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Authors: Alena Kulikova, Leonid Parmaksiz, Ekaterina Orel

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Social and emotional skills are considered by modern researchers as predictors of a person's success both in specific areas of activity and in the life of a person as a whole. The popularity of this scientific direction ensures the emergence of a large number of practices aimed at developing and evaluating socio-emotional skills. Assessment of social and emotional development is carried out at the national level, as well as at the level of individual regions and institutions. Despite the fact that many of the already existing social and emotional skills assessment tools are quite convenient and reliable, there are now more and more new technologies and task formats which improve the basic characteristics of the tools. Thus, the goal of the current study is to develop a tool for assessing social and emotional skills such as emotion recognition, emotion regulation, empathy and a culture of self-care. To develop a tool assessing social and emotional skills, Rasch-Gutman scenario-based approach was used. This approach has shown its reliability and merit for measuring various complex constructs: parental involvement; teacher practices that support cultural diversity and equity; willingness to participate in the life of the community after psychiatric rehabilitation; educational motivation and others. To assess emotion recognition, we used a situational judgment task based on OCC (Ortony, Clore, and Collins) emotions theory. The main advantage of these two approaches compare to classical Likert scales is that it reduces social desirability in answers. A field test to check the psychometric properties of the developed instrument was conducted. The instrument was developed for the presidential autonomous non-profit organization “Russia - Land of Opportunity” for nationwide soft skills assessment among higher education students. The sample for the field test consisted of 500 people, students aged from 18 to 25 (mean = 20; standard deviation 1.8), 71% female. 67% of students are only studying and are not currently working and 500 employed adults aged from 26 to 65 (mean = 42.5; SD 9), 57% female. Analysis of the psychometric characteristics of the scales was carried out using the methods of IRT (Item Response Theory). A one-parameter rating scale model RSM (Rating scale model) and Graded Response model (GRM) of the modern testing theory were applied. GRM is a polyatomic extension of the dichotomous two-parameter model of modern testing theory (2PL) based on the cumulative logit function for modeling the probability of a correct answer. The validity of the developed scales was assessed using correlation analysis and MTMM (multitrait-multimethod matrix). The developed instrument showed good psychometric quality and can be used by HR specialists or educational management. The detailed results of a psychometric study of the quality of the instrument, including the functioning of the tasks of each scale, will be presented. Also, the results of the validity study by MTMM analysis will be discussed.

Keywords: social and emotional skills, psychometrics, MTMM, IRT

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Authors: Tsang K. L. V., Lewis L. A., Griffith S., Tucker P.

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Background: Many children with high-incidence disabilities, such as autism spectrum disorder (ASD), struggle to participate in the community in a socially acceptable manner. There are limitations for clinical settings to provide natural, real-life scenarios for them to practice the life skills needed to meet their real-life challenges. Virtual reality (VR) offers potential solutions to resolve the existing limitation faced by clinicians to create simulated natural environments for their clients to generalize the facilitated skills. Research design: The research aimed to develop a prototype of an interactive VR system to provide realistic and immersive environments for clients to practice skills. The descriptive qualitative methodology is employed to design and develop the Artificial Interactive Reality Unified System (AIRUS) prototype, which provided insights on how to use advanced VR technology to create simulated real-life social scenarios and enable users to interact with the objects and people inside the virtual environment using natural eye-gazes, hand and body movements. The eye tracking (e.g., selective or joint attention), hand- or body-tracking (e.g., repetitive stimming or fidgeting), and facial tracking (e.g., emotion recognition) functions allowed behavioral data to be captured and managed in the AIRUS architecture. Impact of project: Instead of using external controllers or sensors, hand tracking software enabled the users to interact naturally with the simulated environment using daily life behavior such as handshaking and waving to control and interact with the virtual objects and people. The newly developed AIRUS protocol offers opportunities for breakthroughs in future VR-based psychosocial assessment and intervention in occupational therapy. Implications for future projects: AI technology can allow more efficient data capturing and interpretation of object identification and human facial emotion recognition at any given moment. The data points captured can be used to pinpoint our users’ focus and where their interests lie. AI can further help advance the data interpretation system.

Keywords: occupational therapy, psychosocial assessment and intervention, simulated interactive environment, virtual reality

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Authors: Yao-Hong Tsai

Abstract:

This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Cristina Costescu, Carmen David, Adrian Roșan

Abstract:

Executive functions (EF) and emotion regulation strategies are processes that allow individuals to function in an adaptative way and to be goal-oriented, which is essential for success in daily living activities, at school, or in social contexts. The Emotion Regulation and Executive Functioning Scale for Children and Adolescents (REMEX) represents an empirically based tool (based on the model of EF developed by Diamond) for evaluating significant dimensions of child and adolescent EFs and emotion regulation strategies, mainly in school contexts. The instrument measures the following dimensions: working memory, inhibition, cognitive flexibility, executive attention, planning, emotional control, and emotion regulation strategies. Building the instrument involved not only a top-down process, as we selected the content in accordance with prominent models of FE, but also a bottom-up one, as we were able to identify valid contexts in which FE and ER are put to use. For the construction of the instrument, we implemented three focus groups with teachers and other professionals since the aim was to develop an accurate, objective, and ecological instrument. We used the focus group method in order to address each dimension and to yield a bank of items to be further tested. Each dimension is addressed through a task that the examiner will apply and through several items derived from the main task. For the validation of the instrument, we plan to use item response theory (IRT), also known as the latent response theory, that attempts to explain the relationship between latent traits (unobservable cognitive processes) and their manifestations (i.e., observed outcomes, responses, or performance). REMEX represents an ecological scale that integrates a current scientific understanding of emotion regulation and EF and is directly applicable to school contexts, and it can be very useful for developing intervention protocols. We plan to test his convergent validity with the Childhood Executive Functioning Inventory (CHEXI) and Emotion Dysregulation Inventory (EDI) and divergent validity between a group of typically developing children and children with neurodevelopmental disorders, aged between 6 and 9 years old. In a previous pilot study, we enrolled a sample of 40 children with autism spectrum disorders and attention-deficit/hyperactivity disorder aged 6 to 12 years old, and we applied the above-mentioned scales (CHEXI and EDI). Our results showed that deficits in planning, bebavior regulation, inhibition, and working memory predict high levels of emotional reactivity, leading to emotional and behavioural problems. Considering previous results, we expect our findings to provide support for the validity and reliability of the REMEX version as an ecological instrument for assessing emotion regulation and EF in children and for key features of its uses in intervention protocols.

Keywords: executive functions, emotion regulation, children, item response theory, focus group

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

Abstract:

Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

Abstract:

A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

Procedia PDF Downloads 111