Search results for: face recognition

Authors: Gintare Judzentyte

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The present research is aimed to discuss semantics and pragmatics of spatial deictics (deictic adverbs of place and demonstrative pronouns) in the Baltic languages: in spoken Lithuanian and in spoken Latvian. The following objectives have been identified to achieve the aim: 1) to determine the usage of adverbs of place in spoken Lithuanian and Latvian and to verify their meanings in face-to-face communication; 2) to determine the usage of demonstrative pronouns in spoken Lithuanian and Latvian and to verify their meanings in face-to-face communication; 3) to compare the systems between the two spoken languages and to identify the main tendencies. As meanings of demonstratives (adverbs of place and demonstrative pronouns) are context-bound, it is necessary to verify their usage in spontaneous interaction. Besides, deictic gestures play a very important role in face-to-face communication. Therefore, an experimental method is necessary to collect the data. Video material representing spoken Lithuanian and spoken Latvian was recorded by means of the method of a qualitative interview (a semi-structured interview: an empirical research is all about asking right questions). The collected material was transcribed and evaluated taking into account several approaches: 1) physical distance (location of the referent, visual accessibility of the referent); 2) deictic gestures (the combination of language and gesture is especially characteristic of the exophoric use); 3) representation of mental spaces in physical space (a speaker sometimes wishes to mark something that is psychically close as psychologically distant and vice versa). The research of the collected data revealed that in face-to-face communication the participants choose deictic adverbs of place instead of demonstrative pronouns to locate/identify entities in situations where the demonstrative pronouns would be expected in spoken Lithuanian and in spoken Latvian. The analysis showed that visual accessibility of the referent is very important in face-to-face communication, but the main criterion while localizing objects and entities is the need for contrast: lith. čia ‘here’, šis ‘this’, latv. šeit ‘here’, šis ‘this’ usually identify distant entities and are used instead of distal demonstratives (lith. ten ‘there’, tas ‘that’, latv. tur ‘there’, tas ‘that’), because the referred objects/subjects contrast to further entities. Furthermore, the interlocutors in examples from a spontaneously situated interaction usually extend their space and can refer to a ‘distal’ object/subject with a ‘proximal’ demonstrative based on the psychological choice. As the research of the spoken Baltic languages confirmed, the choice of spatial deictics in face-to-face communication is strongly effected by a complex of criteria. Although there are some main tendencies, the exact meaning of spatial deictics in the spoken Baltic languages is revealed and is relevant only in a certain context.

Keywords: Baltic languages, face-to-face communication, pragmatics, semantics, spatial deictics

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Authors: Sonia Bernardo Correia

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This paper is part of ongoing research that aims to understand the role of gender in the composition of the Portuguese contemporary art world and the possibilities and limits to the success of the professional paths of women and men artists. The field of visual arts is gender-sensitive as it differentiates the positions occupied by artists in terms of visibility and recognition. Women artists occupy a peripheral space, which may hinder the progression of their professional careers. Based on the collection of data on the participation of artists in Portuguese exhibitions, art fairs, auctions, and art awards between 2012 and 2019, the goal of this study is to portray female artists’ participation as a condition of professional, social, and cultural visibility. From the analysis of a significant sample of institutions from the artistic field, it was possible to observe that the works of female authors are under exhibited, never exceeding one-third of the total of exhibitions. Male artists also enjoy a comfortable majority as gallery artists (around 70%) and as part of institutional collections (around 80%). However, when analysing the younger age cohorts of artists by gender, it appears that there is representation parity, which may be a good sign of change. The data shows that there are persistent gender inequalities in accessing the artist profession. Women are not yet occupying positions of exposure, recognition, and legitimation in the market similar to those of their male counterparts, suggesting that they may face greater obstacles in experiencing successful professional trajectories.

Keywords: inequalities, invisibility of the woman artist, gender, visual arts

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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Authors: Crisanto H. Ecaldre

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Gender perception represents how an individual perceives the gender identity of a person. Since this is a subjective assessment, it paves the way to various social reactions, either in the form of acceptance or discrimination. Reports across the world show that lesbian, gay, bisexual, or transgender (LGBT) people often face discrimination, stigmatization, and targeted violence because of their sexual orientation or gender identity. However, the challenges faced by those who belong to both a sexual minority and a marginalized ethnic, religious, linguistic, or indigenous community are even more complex. Specifically, in Palaw’an community, members own those who identify themselves as gays or lesbians and use “bantut” to identify them. There was also the introduction of various scholarly works to facilitate dialogues that promote visibility and inclusivity across sectors in terms of gender preferences; however, there are still gaps that need to be addressed in terms of recognition and visibility. Though local research initiatives are slowly increasing in terms of numbers, culturally situating gender studies appropriately within the context of indigenous cultural communities is still lacking. Indigenous community-based discourses on gender or indigenizing gender discourses remain a challenge; hence, this study aimed to contribute to addressing these identified gaps. These research objectives were realized through a qualitative approach following an exploratory design. Findings revealed that the Palaw’an indigenous cultural community has an existing concept of homosexuality, which they termed “bantut.” This notion was culturally defined by the participants as (a) kaloob ng diwata; (b) a manifestation of physical inferiority; (c) hindi nakapag-asawa or hindi nagka-anak; and (d) based on the ascribed roles by the community. These were recognized and valued by the community. However, despite the recognition and visibility within the community, the outside people view them otherwise. The challenges experienced by the Palaw’an homosexuals are imposed by the people outside their community, and these include prejudice, discrimination, and double marginalization. Because of these struggles, they are forced to cope. They deal with these imposed limitations, biases, and burdens by non-Palaw’an through self-acceptance, strong self-perception, and the option to leave the community to seek a more open and progressive environment for LGBTs. While these are indications of their ‘resilience’ amidst difficult situations, this reality poses an important concern -how the recognition and visibility of indigenous homosexuals from the mainstream perspective can be attained.

Keywords: gender preference, acceptability, challenge, recognition, visibility, coping

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Yingjin Cui, Xue Bai, Serena Reese

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Student engagement and student interest during class instruction are important conditions for active learning. Engagement, which has an important relationship with learning motivation, influences students' levels of persistence in overcoming challenges. Lack of student engagement and absence from face-to-face lectures and tutorials, in turn, can lead to poor academic performance. However, keeping students motivated and engaged in the learning process in different instructional modes poses a significant challenge; students can easily become discouraged from attending lectures and tutorials across both online and face-to-face settings. Many factors impact students’ engagement in the learning process. If you want to keep students focused on learning, you have to invite them into the process of helping themselves by providing an active learning environment. Active learning is an excellent technique for enhancing student engagement and participation in the learning process because it provides means to motivate the student to engage themselves in the learning process through reflection, analyzing, applying, and synthesizing the material they learn during class. In this study, we discussed how to create an active learning class (both face-to-face and synchronous online) through engagement activities, including reflection, collaboration, screen messages, open poll, tournament, and transferring editing roles. These activities will provide an uncommon interactive learning environment that can result in improved learning outcomes. To evaluate the effectiveness of those engagement activities in the learning process, an experimental group and a control group will be explored in the study.

Keywords: active learning, academic performance, engagement activities, learning motivation

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Authors: Jesus Alfredo Ortega Granados, Pandiyan Thangarasu

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Antibiotic tetracycline presents as a micro-contaminant in fresh water, wastewater and soils, causing environmental and health problems. In this work, tetracycline (TC) has been employed as chemo-sensor for the recognition of Al³⁺ without interring other ions, and the results show that it enhances the fluorescence intensity for Al³⁺ and there is no interference from other coexisting cation ions (Cd²⁺, Ni²⁺, Co²⁺, Sr²⁺, Mg²⁺, Fe³⁺, K⁺, Sm³⁺, Ag⁺, Na⁺, Ba²⁺, Zn²⁺, and Mn²⁺). For the addition of Cu²⁺ to [TET-Al³⁺], it appears that the intensity of fluorescence has been quenched. Other combinations of metal ions in addition to TC do not change the fluorescence behavior. The stoichiometry determined by Job´s plot for the interaction of TC with Al³⁺ was found to be 1:1. Importantly, the detection of Al³⁺⁺ successfully employed in the real samples like living cells, and it was found that TC efficiently performs as a fluorescent probe for Al³⁺ ion in living systems, especially in Saccharomyces cerevisiae; this is confirmed by confocal laser scanning microscopy.

Keywords: chemo-sensor, recognition of Al³⁺ ion, Saccharomyces cerevisiae, tetracycline,

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Authors: R. R. Hordijk, O. J. G. Somsen

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Traditionally, radar systems are the eyes and ears of a ship. However, these systems have their drawbacks and nowadays they are extended with systems that work with video and photos. Processing of data from these videos and photos is however very labour-intensive and efforts are being made to automate this process. A major problem when trying to recognize objects in water is that the 'background' is not homogeneous so that traditional image recognition technics do not work well. Main question is, can a method be developed which automate this recognition process. There are a large number of parameters involved to facilitate the identification of objects on such images. One is varying the resolution. In this research, the resolution of some images has been reduced to the extreme value of 1% of the original to reduce clutter before the polynomial fit (pre-processing). It turned out that the searched object was clearly recognizable as its grey value was well above the average. Another approach is to take two images of the same scene shortly after each other and compare the result. Because the water (waves) fluctuates much faster than an object floating in the water one can expect that the object is the only stable item in the two images. Both these methods (pre-processing and comparing two images of the same scene) delivered useful results. Though it is too early to conclude that with these methods all image problems can be solved they are certainly worthwhile for further research.

Keywords: image processing, image recognition, polynomial fit, water

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Authors: Naouel Zoghlami

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Spoken-word recognition involves the simultaneous activation of potential word candidates which compete with each other for final correct recognition. In continuous speech, the activation-competition process gets more complicated due to speech reductions existing at word boundaries. Lexical processing is more difficult in L2 than in L1 because L2 listeners often lack phonetic, lexico-semantic, syntactic, and prosodic knowledge in the target language. In this study, we investigate the on-line lexical segmentation hypotheses that French listeners of L2 English form and then revise as subsequent perceptual evidence is revealed. Our purpose is to shed further light on the processes of L2 spoken-word recognition in context and better understand L2 listening difficulties through a comparison of skilled and unskilled reactions at the point where their working hypothesis is rejected. We use a variant of the gating experiment in which subjects transcribe an English sentence presented in increments of progressively greater duration. The spoken sentence was “And this amazing athlete has just broken another world record”, chosen mainly because it included common reductions and phonetic features in English, such as elision and assimilation. Our preliminary results show that there is an important difference in the manner in which proficient and less-proficient L2 listeners handle connected speech. Less-proficient listeners delay recognition of words as they wait for lexical and syntactic evidence to appear in the gates. Further statistical results are currently being undertaken.

Keywords: gating paradigm, spoken word recognition, online lexical segmentation, L2 listening

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Pia Kastl

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Potentials of blended learning are the flexibility of learning and the possibility to get in touch with lecturers and fellow students on site. By combining face-to-face sessions with digital self-learning units, the learning process can be optimized, and learning success increased. To examine wether blended learning outperforms online and face-to-face teaching, a theory-based questionnaire survey was conducted. The results show that the interaction and cooperation among students is poorly provided in blended learning, and face-to-face teaching performs better in this respect. The aim of this article is to identify concrete suggestions students have for improving cooperation and interaction in blended learning courses. For this purpose, interviews were conducted with students from various academic disciplines in face-to-face, online, or blended learning courses (N= 60). The questions referred to opinions and suggestions for improvement regarding the course design of the respective learning environment. The analysis was carried out by qualitative content analysis. The results show that students perceive the interaction as beneficial to their learning. They verbalize their knowledge and are exposed to different perspectives. In addition, emotional support is particularly important in exam phases. Interaction and cooperation were primarily enabled in the face-to-face component of the courses studied, while there was very limited contact with fellow students in the asynchronous component. Forums offered were hardly used or not used at all because the barrier to asking a question publicly is too high, and students prefer private channels for communication. This is accompanied by the disadvantage that the interaction occurs only among people who already know each other. Creating contacts is not fostered in the blended learning courses. Students consider optimization possibilities as a task of the lecturers in the face-to-face sessions: Here, interaction and cooperation should be encouraged through get-to-know-you rounds or group work. It is important here to group the participants randomly to establish contact with new people. In addition, sufficient time for interaction is desired in the lecture, e.g., in the context of discussions or partner work. In the digital component, students prefer synchronous exchange at a fixed time, for example, in breakout rooms or an MS Teams channel. The results provide an overview of how interaction and cooperation can be implemented in blended learning courses. Positive design possibilities are partly dependent on subject area and course. Future studies could tie in here with a course-specific analysis.

Keywords: blended learning, higher education, hybrid teaching, qualitative research, student learning

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Shruti Hajirnis

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In May 2014, FASB issued Accounting Standards Update (ASU) 2014-09, Revenue from Contracts with Customers (Topic 606), and the International Accounting Standards Board (IASB) issued International Financial Reporting Standards (IFRS) 15, Revenue from Contracts with Customers that will supersede virtually all revenue recognition requirements in IFRS and US GAAP. FASB and the IASB have basically achieved convergence with these standards, with only some minor differences such as collectability threshold, interim disclosure requirements, early application and effective date, impairment loss reversal and nonpublic entity requirements. This paper discusses the impact of five-step model prescribed in new revenue standard on the entities operating in Pharma industry. It also outlines the considerations for these entities while implementing the new standard.

Keywords: revenue recognition, pharma industry, standard, requirements

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Daniel Zaccariello

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Our study explores the usefulness of implementing facial expression recognition capabilities and using the Facial Action Coding System (FACS) in contexts where the other person is wearing a mask. In the communication process, the subjects use a plurality of distinct and autonomous reporting systems. Among them, the system of mimicking facial movements is worthy of attention. Basic emotion theorists have identified the existence of specific and universal patterns of facial expressions related to seven basic emotions -anger, disgust, contempt, fear, sadness, surprise, and happiness- that would distinguish one emotion from another. However, due to the COVID-19 pandemic, we have come up against the problem of having the lower half of the face covered and, therefore, not investigable due to the masks. Facial-emotional behavior is a good starting point for understanding: (1) the affective state (such as emotions), (2) cognitive activity (perplexity, concentration, boredom), (3) temperament and personality traits (hostility, sociability, shyness), (4) psychopathology (such as diagnostic information relevant to depression, mania, schizophrenia, and less severe disorders), (5) psychopathological processes that occur during social interactions patient and analyst. There are numerous methods to measure facial movements resulting from the action of muscles, see for example, the measurement of visible facial actions using coding systems (non-intrusive systems that require the presence of an observer who encodes and categorizes behaviors) and the measurement of electrical "discharges" of contracting muscles (facial electromyography; EMG). However, the measuring system invented by Ekman and Friesen (2002) - "Facial Action Coding System - FACS" is the most comprehensive, complete, and versatile. Our study, carried out on about 1,500 subjects over three years of work, allowed us to highlight how the movements of the hands and upper part of the face change depending on whether the subject wears a mask or not. We have been able to identify specific alterations to the subjects’ hand movement patterns and their upper face expressions while wearing masks compared to when not wearing them. We believe that finding correlations between how body language changes when our facial expressions are impaired can provide a better understanding of the link between the face and body non-verbal language.

Keywords: facial action coding system, COVID-19, masks, facial analysis

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Authors: Everdeanne Javier, Kelvin Louie Abat, Alodia Rizzalynn Cabaya, Chynna Allyson Manzano, Vlasta Sai Espiritu, Raniah May Puzon, Michelle Tobler

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Teledermatology is becoming a more popular form of providing dermatologic healthcare worldwide, and it will almost certainly play a larger role in the future. As the current pandemic continues to worsen, Teledermatology is seen as the primary alternative to face-to-face dermatology consultation; therefore, it needs to be enhanced and developed to become as convenient and reliable as it can be for both patients and doctors. This research paper seeks to know the processes used in teledermatology regarding delivery modalities and proper consultation. This study's research design is a Qualitative Descriptive approach to describe further the processes used by teledermatologists. An online survey questionnaire was used to collect data from Teledermatology Clinics in San Fernando City, La Union. Research showed that patients tend to embrace and be pleased with teledermatology as a way of accessing healthcare. On the other hand, clinicians have usually reported positive outcomes from teledermatology. Furthermore, it is not intended to be used instead of a face-to-face appointment with a dermatologist.

Keywords: teledermatology, online dermatology consultation, dermatology, dermatologist

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Heng Yang, Tao Luo, Yakun Zhang, Kai Wang, Wei Qin, Liang Xie, Ye Yan, Erwei Yin

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Recent years have seen that audio-visual recognition has shown great potential in a strong noise environment. The existing method of audio-visual recognition has explored methods with ResNet and feature fusion. However, on the one hand, ResNet always occupies a large amount of memory resources, restricting the application in engineering. On the other hand, the feature merging also brings some interferences in a high noise environment. In order to solve the problems, we proposed an effective framework with bidirectional distillation. At first, in consideration of the good performance in extracting of features, we chose the light model, Efficientnet as our extractor of spatial features. Secondly, self-distillation was applied to learn more information from raw data. Finally, we proposed a bidirectional distillation in decision-level fusion. In more detail, our experimental results are based on a multi-model dataset from 24 volunteers. Eventually, the lipreading accuracy of our framework was increased by 2.3% compared with existing systems, and our framework made progress in audio-visual fusion in a high noise environment compared with the system of audio recognition without visual.

Keywords: lipreading, audio-visual, Efficientnet, distillation

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

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The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: clustering, edges, feature points, landmark selection, X-means

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Authors: Alaaeldin Hamdy Ahmed Mohammed

Abstract:

Introduction: This study examines sports’ fans and non-interested public perception and recognition of the problems that have negative impacts upon the Egyptian sports, particularly football, through caricatures. Eight caricature paintings were designed to express eight problems affecting the Egyptian sports and its development. These paintings were distributed on two groups of the fans and the non-interested public. Methods: The study was limited to eight caricatures representing the eight issues which are: the impact of stopping the sports activity on athletes, the effect of clubs’ disagreement, fanaticism between the members of the ultras of different clubs, the negative impact of the mingling of politics into sports, the negative role of the clubs affects the professionalism of the promising players, the conflict between the national organization responsible for sports, the breaking in of the fans to the playgrounds, the impact of the lack of planning on the national team. The Results: The results showed that both sports fans and those who are not interested in sports recognized the problems that the caricatures refer to and criticizes exaggeration although the rate was higher for the fans. These caricatures contributed also in their recognition of the danger of the negative impact of these problems on the Egyptian sports, particularly football which is the most common at the Egyptian sports fans. Discussion: This finding echoes the conclusion that caricatures are distinctive in the adults’ facial stimuli that are either systematically exaggerated recognition of them.

Keywords: caricature, fans, football, sports

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

Abstract:

All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

Abstract:

The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

Abstract:

Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Saurabh Farkya, Govinda Surampudi

Abstract:

Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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