Search results for: Low resolution face recognition

Authors: R. Padilla, C. F. F. Costa Filho, M. G. F. Costa

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In the past years a lot of effort has been made in the field of face detection. The human face contains important features that can be used by vision-based automated systems in order to identify and recognize individuals. Face location, the primary step of the vision-based automated systems, finds the face area in the input image. An accurate location of the face is still a challenging task. Viola-Jones framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. An evaluation of these classifiers will help researchers to choose the best classifier for their particular need. This work focuses of the evaluation of face detection classifiers minding facial landmarks.

Keywords: Face datasets, face detection, facial landmarking, haar wavelets, Viola-Jones detectors.

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Authors: Cuong Nguyen Khac, Ju H. Park, Ho-Youl Jung

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This paper proposes a new approach to perform the problem of real-time face detection. The proposed method combines primitive Haar-Like feature and variance value to construct a new feature, so-called Variance based Haar-Like feature. Face in image can be represented with a small quantity of features using this new feature. We used SVM instead of AdaBoost for training and classification. We made a database containing 5,000 face samples and 10,000 non-face samples extracted from real images for learning purposed. The 5,000 face samples contain many images which have many differences of light conditions. And experiments showed that face detection system using Variance based Haar-Like feature and SVM can be much more efficient than face detection system using primitive Haar-Like feature and AdaBoost. We tested our method on two Face databases and one Non-Face database. We have obtained 96.17% of correct detection rate on YaleB face database, which is higher 4.21% than that of using primitive Haar-Like feature and AdaBoost.

Keywords: AdaBoost, Haar-Like feature, SVM, variance, Variance based Haar-Like feature.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: Emotion recognition, facial recognition, signal processing, machine learning.

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Authors: Ramin Halavati, Mansour Jamzad, Mahdieh Soleymani

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Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.

Keywords: Image Processing, Pattern Recognition.

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Authors: Liyakathunisa, V. K. Ananthashayana

Abstract:

Crucial information barely visible to the human eye is often embedded in a series of low resolution images taken of the same scene. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. The ideal algorithm should be fast, and should add sharpness and details, both at edges and in regions without adding artifacts. In this paper we propose a super resolution blind reconstruction technique for linearly degraded images. In our proposed technique the algorithm is divided into three parts an image registration, wavelets based fusion and an image restoration. In this paper three low resolution images are considered which may sub pixels shifted, rotated, blurred or noisy, the sub pixel shifted images are registered using affine transformation model; A wavelet based fusion is performed and the noise is removed using soft thresolding. Our proposed technique reduces blocking artifacts and also smoothens the edges and it is also able to restore high frequency details in an image. Our technique is efficient and computationally fast having clear perspective of real time implementation.

Keywords: Affine Transforms, Denoiseing, DWT, Fusion, Image registration.

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Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

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This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

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Authors: Van Nhan Nguyen, Harald Holone

Abstract:

Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: Automatic Speech Recognition, ASR, Air Traffic Control, ATC.

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Authors: Safdar Tanweer, Abdul Mobin, Afshar Alam

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The performance and analysis of speech recognition system is illustrated in this paper. An approach to recognize the English word corresponding to digit (0-9) spoken by 2 different speakers is captured in noise free environment. For feature extraction, speech Mel frequency cepstral coefficients (MFCC) has been used which gives a set of feature vectors from recorded speech samples. Neural network model is used to enhance the recognition performance. Feed forward neural network with back propagation algorithm model is used. However other speech recognition techniques such as HMM, DTW exist. All experiments are carried out on Matlab.

Keywords: Speech Recognition, MFCC, Neural Network, classifier.

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

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Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: Helen Zhang

Abstract:

Accurate segmentation of the airways from CT scans is crucial for early diagnosis of lung cancer. However, the existing airway segmentation algorithms often rely on thin-slice CT scans, which can be inconvenient and costly. This paper presents a set of machine learning-based 3D super-resolution algorithms along heterogenous dimensions to improve the resolution of thicker CT scans to reduce the reliance on thin-slice scans. To evaluate the efficacy of the super-resolution algorithms, quantitative assessments using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural SIMilarity index) were performed. The impact of super-resolution on airway segmentation accuracy is also studied. The proposed approach has the potential to make airway segmentation more accessible and affordable, thereby facilitating early diagnosis and treatment of lung cancer.

Keywords: 3D super-resolution, airway segmentation, thin-slice CT scans, machine learning.

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Authors: H. H. Huynh, J. Meunier, J.Sequeira, M.Daniel

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In this paper, the detection and tracking of face, mouth, hands and medication bottles in the context of medication intake monitoring with a camera is presented. This is aimed at recognizing medication intake for elderly in their home setting to avoid an inappropriate use. Background subtraction is used to isolate moving objects, and then, skin and bottle segmentations are done in the RGB normalized color space. We use a minimum displacement distance criterion to track skin color regions and the R/G ratio to detect the mouth. The color-labeled medication bottles are simply tracked based on the color space distance to their mean color vector. For the recognition of medication intake, we propose a three-level hierarchal approach, which uses activity-patterns to recognize the normal medication intake activity. The proposed method was tested with three persons, with different medication intake scenarios, and gave an overall precision of over 98%.

Keywords: Activity recognition, background subtraction, tracking, medication intake, video surveillance

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that intersubject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: Person Recognition, Topic Recognition, Culture Recognition, 3D Body Movement Signals, Variability Compensation.

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Authors: Srikanta Bose, Sudip K. Mazumder

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We report here, the results of molecular dynamics simulation of p-doped (Ga-face)GaN over n-doped (Siface)( 0001)4H-SiC hetero-epitaxial material system with one-layer each of Ga-flux and (Al-face)AlN, as the interface materials, in the form of, the total Density of States (DOS). It is found that the total DOS at the Fermi-level for the heavily p-doped (Ga-face)GaN and ndoped (Si-face)4H-SiC hetero-epitaxial system, with one layer of (Al-face)AlN as the interface material, is comparatively higher than that of the various cases studied, indicating that there could be good vertical conduction across the (Ga-face)GaN over (Si-face)(0001)4HSiC hetero-epitaxial material system.

Keywords: Molecular dynamics, GaN, 4H-SiC, hetero-epitaxy.

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Authors: Marziyeh Yousefi, Hossein Nassaji

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This paper reports the results of a meta-analysis of studies on the effects of instruction mode on learning second language pragmatics during the last decade (from 2006 to 2016). After establishing related inclusion/ exclusion criteria, 39 published studies were retrieved and included in the present meta-analysis. Studies were later coded for face-to-face and computer-assisted mode of instruction. Statistical procedures were applied to obtain effect sizes. It was found that Computer-Assisted-Language-Learning studies generated larger effects than Face-to-Face instruction.

Keywords: Meta-analysis, effect size, pragmatics, computer-assisted language learnin, face-to-face instruction, comprehensive meta-analysis software.

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Authors: Kriddikorn Kaewwongsri, Nikom Suvonvorn

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In the deep south of Thailand, checkpoints for people verification are necessary for the security management of risk zones, such as official buildings in the conflict area. In this paper, we propose an automatic checkpoint system that verifies persons using information from ID cards and facial features. The methods for a person’s information abstraction and verification are introduced based on useful information such as ID number and name, extracted from official cards, and facial images from videos. The proposed system shows promising results and has a real impact on the local society.

Keywords: Face comparison, card recognition, OCR, checkpoint system, authentication.

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, stereotypical motor movements, repetitive gesture, kinect, artificial neural network.

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Authors: V. Girondel, L. Bonnaud, A. Caplier, M. Rombaut

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This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.

Keywords: Belief theory, classifiers comparison, data fusion, human motion analysis, real-time processing, static posture recognition.

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Authors: Ahmad T. Al-Taani

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In this paper, an efficient structural approach for recognizing on-line handwritten digits is proposed. After reading the digit from the user, the slope is estimated and normalized for adjacent nodes. Based on the changing of signs of the slope values, the primitives are identified and extracted. The names of these primitives are represented by strings, and then a finite state machine, which contains the grammars of the digits, is traced to identify the digit. Finally, if there is any ambiguity, it will be resolved. Experiments showed that this technique is flexible and can achieve high recognition accuracy for the shapes of the digits represented in this work.

Keywords: Digits Recognition, Pattern Recognition, FeatureExtraction, Structural Primitives, Document Processing, Handwritten Recognition, Primitives Selection.

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Authors: Ozan Mut, Mehmet Göktürk

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Matching algorithms have significant importance in speaker recognition. Feature vectors of the unknown utterance are compared to feature vectors of the modeled speakers as a last step in speaker recognition. A similarity score is found for every model in the speaker database. Depending on the type of speaker recognition, these scores are used to determine the author of unknown speech samples. For speaker verification, similarity score is tested against a predefined threshold and either acceptance or rejection result is obtained. In the case of speaker identification, the result depends on whether the identification is open set or closed set. In closed set identification, the model that yields the best similarity score is accepted. In open set identification, the best score is tested against a threshold, so there is one more possible output satisfying the condition that the speaker is not one of the registered speakers in existing database. This paper focuses on closed set speaker identification using a modified version of a well known matching algorithm. The results of new matching algorithm indicated better performance on YOHO international speaker recognition database.

Keywords: Automatic Speaker Recognition, Voice Recognition, Pattern Recognition, Digital Audio Signal Processing.

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Authors: M. van der Bank, C. M. van der Bank

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Alternative Dispute Resolution denotes all forms of dispute resolution other than litigation or adjudication through the courts. This definition of Alternative Dispute Resolution, however, makes no mention of a vital consideration. ADR is the generally accepted acronym for alternative dispute resolution. Despite the choice not to proceed before a court or statutory tribunal, ADR will still be regulated by law and by the Constitution. Fairness is one of the core values of the South African constitutional order. Environmental disputes occur frequently, but due to delays and costs, ADR is a mechanism to resolve this kind of disputes which is a resolution of non-judicial mechanism. ADR can be used as a mechanism in environmental disputes that are less expensive and also more expeditious than formal litigation. ADR covers a broad range of mechanisms and processes designed to assist parties in resolving disputes creatively and effectively. In so far as this may involve the selection or design of mechanisms and processes other than formal litigation, these mechanisms and processes are not intended to supplant court adjudication, but rather to supplement it. A variety of ADR methods have been developed to deal with numerous problems encountered during environmental disputes. The research questions are: How can ADR facilitate environmental disputes in South Africa? Are they appropriate? And what improvements should be made?

Keywords: Alternative dispute, environmental disputes, non-judicial, resolution and settlement.

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Authors: J. Umamaheswari, G. Radhamani

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This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.

Keywords: Recognition, classification, Relaxed Median Filter, Adaptive thresholding, clustering and Neural Networks

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: Speech recognition, acoustic phonetic, artificial intelligence, Hidden Markov Models (HMM), statistical models of speech recognition, human machine performance.

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Authors: S. Sarumathi, M. Vaishnavi, S. Geetha, P. Ranjetha

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Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey.

Keywords: Artificial intelligence, machine learning, deep learning, machine learning algorithms, machine learning tools.

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Authors: P. Phokharatkul, K. Sankhuangaw, S. Somkuarnpanit, S. Phaiboon, C. Kimpan

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Much research into handwritten Thai character recognition have been proposed, such as comparing heads of characters, Fuzzy logic and structure trees, etc. This paper presents a system of handwritten Thai character recognition, which is based on the Ant-minor algorithm (data mining based on Ant colony optimization). Zoning is initially used to determine each character. Then three distinct features (also called attributes) of each character in each zone are extracted. The attributes are Head zone, End point, and Feature code. All attributes are used for construct the classification rules by an Ant-miner algorithm in order to classify 112 Thai characters. For this experiment, the Ant-miner algorithm is adapted, with a small change to increase the recognition rate. The result of this experiment is a 97% recognition rate of the training set (11200 characters) and 82.7% recognition rate of unseen data test (22400 characters).

Keywords: Hand written, Thai character recognition, Ant-mineralgorithm, distinct feature.

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Authors: Jungho Choi, Youngwan Cho

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The paper proposes a way of parallel processing of SURF and Optical Flow for moving object recognition and tracking. The object recognition and tracking is one of the most important task in computer vision, however disadvantage are many operations cause processing speed slower so that it can-t do real-time object recognition and tracking. The proposed method uses a typical way of feature extraction SURF and moving object Optical Flow for reduce disadvantage and real-time moving object recognition and tracking, and parallel processing techniques for speed improvement. First analyse that an image from DB and acquired through the camera using SURF for compared to the same object recognition then set ROI (Region of Interest) for tracking movement of feature points using Optical Flow. Secondly, using Multi-Thread is for improved processing speed and recognition by parallel processing. Finally, performance is evaluated and verified efficiency of algorithm throughout the experiment.

Keywords: moving object recognition, moving object tracking, SURF, Optical Flow, Multi-Thread.

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Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou

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Emotion in speech is an issue that has been attracting the interest of the speech community for many years, both in the context of speech synthesis as well as in automatic speech recognition (ASR). In spite of the remarkable recent progress in Large Vocabulary Recognition (LVR), it is still far behind the ultimate goal of recognising free conversational speech uttered by any speaker in any environment. Current experimental tests prove that using state of the art large vocabulary recognition systems the error rate increases substantially when applied to spontaneous/emotional speech. This paper shows that recognition rate for emotionally coloured speech can be improved by using a language model based on increased representation of emotional utterances.

Keywords: Statistical language model, N-grams, emotionallycoloured speech

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Authors: Bremananth R, Nithya B, Saipriya R

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The proposed system identifies the species of the wood using the textural features present in its barks. Each species of a wood has its own unique patterns in its bark, which enabled the proposed system to identify it accurately. Automatic wood recognition system has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In our work, a wood recognition system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition, rock classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. The result thus obtained shows a high rate of recognition accuracy proving that the techniques used in suitable to be implemented for commercial purposes.

Keywords: Correlation, Grey Level Co-Occurrence Matrix, ProbabilityDensity Function, Wood Recognition.

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: Deep learning network, smart metering, water end use, water-energy data.

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Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim

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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.

Keywords: Face Alignment, AAM, facial feature detection, model matching.

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Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

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This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: Extreme learning, LIRA neural classifier, speaker identification, voice recognition.

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