High Performance Techniques for Face Recognition

The identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, makeup, rotations, illuminations, gestures, etc. Also, facial images contain a great deal of redundant information, which negatively affects the performance of the recognition system. The dimensionality and the redundancy of the facial features have a direct effect on the face recognition accuracy. Not all the features in the feature vector space are useful. For example, non-discriminating features in the feature vector space not only degrade the recognition accuracy but also increase the computational complexity. In the field of computer vision, pattern recognition, and image processing, face recognition has become a popular research topic. This is due to its wide spread applications in security and control, which allow the identified individual to access secure areas, personal information, etc. The performance of any recognition system depends on three factors: 1) the storage requirements, 2) the computational complexity, and 3) the recognition rates. Two different recognition system families are presented and developed in this dissertation. Each family consists of several face recognition systems. Each system contains three main steps, namely, preprocessing, feature extraction, and classification. Several preprocessing steps, such as cropping, facial detection, dividing the facial image into sub-images, etc. are applied to the facial images. This reduces the effect of the irrelevant information (background) and improves the system performance. In this dissertation, either a Neural Network (NN) based classifier or Euclidean distance is used for classification purposes. Five widely used databases, namely, ORL, YALE, FERET, FEI, and LFW, each containing different facial variations, such as light condition, rotations, facial expressions, facial details, etc., are used to evaluate the proposed systems. The experimental results of the proposed systems are analyzed using K-folds Cross Validation (CV). In the family-1, Several systems are proposed for face recognition. Each system employs different integrated tools in the feature extraction step. These tools, Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), 2D or 3D DWT, and Fast Independent Component Analysis (FastICA), are applied to the processed facial images to reduce the dimensionality and to obtain discriminating features. Each proposed system produces a unique representation, and achieves less storage requirements and better performance than the existing methods. For further facial compression, there are three face recognition systems in the second family. Each system uses different integrated tools to obtain better facial representation. The integrated tools, Vector Quantization (VQ), Discrete cosine Transform (DCT), and 2D DWT, are applied to the facial images for further facial compression and better facial representation. In the systems using the tools VQ/2D DCT and VQ/ 2D DWT, each pose in the databases is represented by one centroid with 4*4*16 dimensions. In the third system, VQ/ Facial Part Detection (FPD), each person in the databases is represented by four centroids with 4*Centroids (4*4*16) dimensions. The systems in the family-2 are proposed to further reduce the dimensions of the data compared to the systems in the family-1 while attaining comparable results. For example, in family-1, the integrated tools, FastICA/ 2D DMWT, applied to different combinations of sub-images in the FERET database with K-fold=5 (9 different poses used in the training mode), reduce the dimensions of the database by 97.22% and achieve 99% accuracy. In contrast, the integrated tools, VQ/ FPD, in the family-2 reduce the dimensions of the data by 99.31% and achieve 97.98% accuracy. In this example, the integrated tools, VQ/ FPD, accomplished further data compression and less accuracy compared to those reported by FastICA/ 2D DMWT tools. Various experiments and simulations using MATLAB are applied. The experimental results of both families confirm the improvements in the storage requirements, as well as the recognition rates as compared to some recently reported methods

Different Facial Recognition Techniques in Transform Domains

The human face is frequently used as the biometric signal presented to a machine for identification purposes. Several challenges are encountered while designing face identification systems. The challenges are either caused by the process of capturing the face image itself, or occur while processing the face poses. Since the face image not only contains the face, this adds to the data dimensionality, and thus degrades the performance of the recognition system. Face Recognition (FR) has been a major signal processing topic of interest in the last few decades. Most common applications of the FR include, forensics, access authorization to facilities, or simply unlocking of a smart phone. The three factors governing the performance of a FR system are: the storage requirements, the computational complexity, and the recognition accuracy. The typical FR system consists of the following main modules in each of the Training and Testing phases: Preprocessing, Feature Extraction, and Classification. The ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech datasets are used to evaluate the performance of the proposed systems. The proposed systems are categorized into Single-Transform and Two-Transform systems. In the first category, the features are extracted from a single domain, that of the Two-Dimensional Discrete Cosine Transform (2D DCT). In the latter category, the Two-Dimensional Discrete Wavelet Transform (2D DWT) coefficients are combined with those of the 2D DCT to form one feature vector. The feature vectors are either used directly or further processed to obtain the persons\u27 final models. The Principle Component Analysis (PCA), the Sparse Representation, Vector Quantization (VQ) are employed as a second step in the Feature Extraction Module. Additionally, a technique is proposed in which the feature vector is composed of appropriately selected 2D DCT and 2D DWT coefficients based on a residual minimization algorithm

Brainwave-Based Human Emotion Estimation using Deep Neural Network Models for Biofeedback

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEmotion is a state that comprehensively represents human feeling, thought and behavior, thus takes an important role in interpersonal human communication. Emotion estimation aims to automatically discriminate different emotional states by using physiological and nonphysiological signals acquired from human to achieve effective communication and interaction between human and machines. Brainwaves-Based Emotion Estimation is one of the most common used and efficient methods for emotion estimation research. The technology reveals a great role for human emotional disorder treatment, brain computer interface for disabilities, entertainment and many other research areas. In this thesis, various methods, schemes and frameworks are presented for Electroencephalogram (EEG) based human emotion estimation. Firstly, a hybrid dimension featurere duction scheme is presented using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re-order the combined features into max-relevance with the emotion labels and min-redundancy of each feature. The generated features are further reduced with Principal Component Analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state-of-art methods using the same settings at the publicly available Database for Emotional Analysis using Physiological Signals (DEAP) data set. Secondly, a disentangled adaptive noise learning β-Variational autoencoder (VAE) combinewithlongshorttermmemory(LSTM)modelwasproposedfortheemotionrecognition based on EEG recordings. The experiment is also based on the EEG emotion public DEAPdataset. At first, the EEG time-series data are transformed into the Video-like EEG image data through the Azimuthal Equidistant Projection (AEP) to original EEG-sensor 3-D coordinates to perform 2-D projected locations of electrodes. Then Clough-Tocher scheme is applied for interpolating the scattered power measurements over the scalp and for estimating the values in-between the electrodes over a 32x32 mesh. After that, the βVAE LSTM algorithm is used to estimate the accuracy of the quadratic (arousal-valence) classification. The comparison between the β VAE-LSTM model and other classic methods is conducted at the same experimental setting that shows that the proposed model is effective. Finally, a novel real-time emotion detection system based on the EEG signals from a portable headband was presented, integrated into the interactive film ‘RIOT’. At first, the requirement of the interactive film was collected and the protocol for data collection using a portable EEG sensor (Emotiv Epoc) was designed. Then, a portable EEG emotion database (PEED) is built from 10 participants with the emotion labels using both self-reporting and video annotation tools. After that, various feature extraction, feature selection, validation scheme and classification methods are explored to build a practical system for the real-time detection. In the end, the emotion detection system is trained and integrated into the interactive film for real-time implementation and fully evaluated. The experimental results demonstrate the system with satisfied emotion detection accuracy and real-time performance

Bio-Signal Analysis in Fatigue and Cancer Related Fatigue;Weakening of Corticomuscular Functional Coupling

Fatigue is a common experience that reduces productivity and increases chance of injury, and has been reported as one of most common symptoms with greatest impact on quality-of-life parameters in cancer patients. Neural mechanisms behind fatigue and cancer related fatigue (CRF) are not well known. Recent research has shown dissociation between changes in brain and muscle signals during voluntary muscle fatigue, which may suggest weakening of functional corticomuscular coupling (fCMC). However, this weakening of brain-muscle coupling has never been directly evaluated. More important information could be gained if fCMC is directly detected during fatigue because a voluntary muscle contraction depends on integration of the entire chain of events and is a complex interaction of different components from the central nervous system to peripheral systems. This research, first, evaluated the effect of muscle fatigue on fCMC in healthy people by determining electroencephalography (EEG)-electromyography (EMG) coherence during two stages of a sustained voluntary muscle contraction, one with minimal fatigue and the other with severer fatigue. The obtained results suggest that despite an elevation of the power for both the EEG and EMG activities with muscle fatigue, the fatigue weakens strength of fCMC between the two signals. Secondly, given the fact that there is larger discrepancy between central and peripheral fatigue in CRF, the effect of cancer related fatigue on fCMC was evaluated by comparing EEG-EMG coherence during a muscle fatigue task in CRF patients with healthy controls. CRF patients showed significantly lower fCMC compared to healthy controls during minimal fatigue stage which may be caused by possible pathophysiological impairments in the patients. Finally, to better understand dynamic fatigue effect on fCMC, a single trial coherence estimation based on Morlet wavelet was developed and applied to investigate fatigue effect on fCMC in single trial during repetitive maximal muscle contractions. It was reveale

EmoEEG - recognising people's emotions using electroencephalography

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas), Universidade de Lisboa, Faculdade de Ciências, 2020As emoções desempenham um papel fulcral na vida humana, estando envolvidas numa extensa variedade de processos cognitivos, tais como tomada de decisão, perceção, interações sociais e inteligência. As interfaces cérebro-máquina (ICM) são sistemas que convertem os padrões de atividade cerebral de um utilizador em mensagens ou comandos para uma determinada aplicação. Os usos mais comuns desta tecnologia permitem que pessoas com deficiência motora controlem braços mecânicos, cadeiras de rodas ou escrevam. Contudo, também é possível utilizar tecnologias ICM para gerar output sem qualquer controle voluntário. A identificação de estados emocionais é um exemplo desse tipo de feedback. Por sua vez, esta tecnologia pode ter aplicações clínicas tais como a identificação e monitorização de patologias psicológicas, ou aplicações multimédia que facilitem o acesso a músicas ou filmes de acordo com o seu conteúdo afetivo. O interesse crescente em estabelecer interações emocionais entre máquinas e pessoas, levou à necessidade de encontrar métodos fidedignos de reconhecimento emocional automático. Os autorrelatos podem não ser confiáveis devido à natureza subjetiva das próprias emoções, mas também porque os participantes podem responder de acordo com o que acreditam que os outros responderiam. A fala emocional é uma maneira eficaz de deduzir o estado emocional de uma pessoa, pois muitas características da fala são independentes da semântica ou da cultura. No entanto, a precisão ainda é insuficiente quando comparada com outros métodos, como a análise de expressões faciais ou sinais fisiológicos. Embora o primeiro já tenha sido usado para identificar emoções com sucesso, ele apresenta desvantagens, tais como o fato de muitas expressões faciais serem "forçadas" e o fato de que as leituras só são possíveis quando o rosto do sujeito está dentro de um ângulo muito específico em relação à câmara. Por estes motivos, a recolha de sinais fisiológicos tem sido o método preferencial para o reconhecimento de emoções. O uso do EEG (eletroencefalograma) permite-nos monitorizar as emoções sentidas sob a forma de impulsos elétricos provenientes do cérebro, permitindo assim obter uma ICM para o reconhecimento afetivo. O principal objetivo deste trabalho foi estudar a combinação de diferentes elementos para identificar estados afetivos, estimando valores de valência e ativação usando sinais de EEG. A análise realizada consistiu na criação de vários modelos de regressão para avaliar como diferentes elementos afetam a precisão na estimativa de valência e ativação. Os referidos elementos foram os métodos de aprendizagem automática, o género do indivíduo, o conceito de assimetria cerebral, os canais de elétrodos utilizados, os algoritmos de extração de características e as bandas de frequências analisadas. Com esta análise foi possível criarmos o melhor modelo possível, com a combinação de elementos que maximiza a sua precisão. Para alcançar os nossos objetivos, recorremos a duas bases de dados (AMIGOS e DEAP) contendo sinais de EEG obtidos durante experiências de desencadeamento emocional, juntamente com a autoavaliação realizada pelos respetivos participantes. Nestas experiências, os participantes visionaram excertos de vídeos de conteúdo afetivo, de modo a despoletar emoções sobre eles, e depois classificaram-nas atribuindo o nível de valência e ativação experienciado. Os sinais EEG obtidos foram divididos em epochs de 4s e de seguida procedeu-se à extração de características através de diferentes algoritmos: o primeiro, segundo e terceiro parâmetros de Hjorth; entropia espectral; energia e entropia de wavelets; energia e entropia de FMI (funções de modos empíricos) obtidas através da transformada de Hilbert-Huang. Estes métodos de processamento de sinal foram escolhidos por já terem gerado resultados bons noutros trabalhos relacionados. Todos estes métodos foram aplicados aos sinais EEG dentro das bandas de frequência alfa, beta e gama, que também produziram bons resultados de acordo com trabalhos já efetuados. Após a extração de características dos sinais EEG, procedeu-se à criação de diversos modelos de estimação da valência e ativação usando as autoavaliações dos participantes como “verdade fundamental”. O primeiro conjunto de modelos criados serviu para aferir quais os melhores métodos de aprendizagem automática a utilizar para os testes vindouros. Após escolher os dois melhores, tentámos verificar as diferenças no processamento emocional entre os sexos, realizando a estimativa em homens e mulheres separadamente. O conjunto de modelos criados a seguir visou testar o conceito da assimetria cerebral, que afirma que a valência emocional está relacionada com diferenças na atividade fisiológica entre os dois hemisférios cerebrais. Para este teste específico, foram consideradas a assimetria diferencial e racional segundo pares de elétrodos homólogos. Depois disso, foram criados modelos de estimação de valência e ativação considerando cada um dos elétrodos individualmente. Ou seja, os modelos seriam gerados com todos os métodos de extração de características, mas com os dados obtidos de um elétrodo apenas. Depois foram criados modelos que visassem comparar cada um dos algoritmos de extração de características utilizados. Os modelos gerados nesta fase incluíram os dados obtidos de todos os elétrodos, já que anteriormente se verificou que não haviam elétrodos significativamente melhores que outros. Por fim, procedeu-se à criação dos modelos com a melhor combinação de elementos possível, otimizaram-se os parâmetros dos mesmos, e procurámos também aferir a sua validação. Realizámos também um processo de classificação emocional associando cada par estimado de valores de valência e ativação ao quadrante correspondente no modelo circumplexo de afeto. Este último passo foi necessário para conseguirmos comparar o nosso trabalho com as soluções existentes, pois a grande maioria delas apenas identificam o quadrante emocional, não estimando valores para a valência e ativação. Em suma, os melhores métodos de aprendizagem automática foram RF (random forest) e KNN (k-nearest neighbours), embora a combinação dos melhores métodos de extração de características fosse diferente para os dois. KNN apresentava melhor precisão considerando todos os métodos de extração menos a entropia espectral, enquanto que RF foi mais preciso considerando apenas o primeiro parâmetro de Hjorth e a energia de wavelets. Os valores dos coeficientes de Pearson obtidos para os melhores modelos otimizados ficaram compreendidos entre 0,8 e 0,9 (sendo 1 o valor máximo). Não foram registados melhoramentos nos resultados considerando cada género individualmente, pelo que os modelos finais foram criados usando os dados de todos os participantes. É possível que a diminuição da precisão dos modelos criados para cada género seja resultado da menor quantidade de dados envolvidos no processo de treino. O conceito de assimetria cerebral só foi útil nos modelos criados usando a base de dados DEAP, especialmente para a estimação de valência usando as características extraídas segundo a banda alfa. Em geral, as nossas abordagens mostraram-se a par ou mesmo superiores a outros trabalhos, obtendo-se valores de acurácia de 86.5% para o melhor modelo de classificação gerado com a base de dados AMIGOS e 86.6% usando a base de dados DEAP.Emotion recognition is a field within affective computing that is gaining increasing relevance and strives to predict an emotional state using physiological signals. Understanding how these biological factors are expressed according to one’s emotions can enhance the humancomputer interaction (HCI). This knowledge, can then be used for clinical applications such as the identification and monitoring of psychiatric disorders. It can also be used to provide better access to multimedia content, by assigning affective tags to videos or music. The goal of this work was to create several models for estimating values of valence and arousal, using features extracted from EEG signals. The different models created were meant to compare how various elements affected the accuracy of the model created. These elements were the machine learning techniques, the gender of the individual, the brain asymmetry concept, the electrode channels, the feature extraction methods and the frequency of the brain waves analysed. The final models contained the best combination of these elements and achieved PCC values over 0.80. As a way to compare our work with previous approaches, we also implemented a classification procedure to find the correspondent quadrant in the valence and arousal space according to the circumplex model of affect. The best accuracies achieved were over 86%, which was on par or even superior to some of the works already done

Automatic face recognition using stereo images

Face recognition is an important pattern recognition problem, in the study of both natural and artificial learning problems. Compaxed to other biometrics, it is non-intrusive, non- invasive and requires no paxticipation from the subjects. As a result, it has many applications varying from human-computer-interaction to access control and law-enforcement to crowd surveillance. In typical optical image based face recognition systems, the systematic vaxiability arising from representing the three-dimensional (3D) shape of a face by a two-dimensional (21)) illumination intensity matrix is treated as random vaxiability. Multiple examples of the face displaying vaxying pose and expressions axe captured in different imaging conditions. The imaging environment, pose and expressions are strictly controlled and the images undergo rigorous normalisation and pre-processing. This may be implemented in a paxtially or a fully automated system. Although these systems report high classification accuracies (>90%), they lack versatility and tend to fail when deployed outside laboratory conditions. Recently, more sophisticated 3D face recognition systems haxnessing the depth information have emerged. These systems usually employ specialist equipment such as laser scanners and structured light projectors. Although more accurate than 2D optical image based recognition, these systems are equally difficult to implement in a non-co-operative environment. Existing face recognition systems, both 2D and 3D, detract from the main advantages of face recognition and fail to fully exploit its non-intrusive capacity. This is either because they rely too much on subject co-operation, which is not always available, or because they cannot cope with noisy data. The main objective of this work was to investigate the role of depth information in face recognition in a noisy environment. A stereo-based system, inspired by the human binocular vision, was devised using a pair of manually calibrated digital off-the-shelf cameras in a stereo setup to compute depth information. Depth values extracted from 2D intensity images using stereoscopy are extremely noisy, and as a result this approach for face recognition is rare. This was cofirmed by the results of our experimental work. Noise in the set of correspondences, camera calibration and triangulation led to inaccurate depth reconstruction, which in turn led to poor classifier accuracy for both 3D surface matching and 211) 2 depth maps. Recognition experiments axe performed on the Sheffield Dataset, consisting 692 images of 22 individuals with varying pose, illumination and expressions

Automated classification of cancer tissues using multispectral imagery

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting machine learning algorithms to classify microscopic images of tumour biopsies. Recently, multispectral imagery has received a significant interest from the research community due to the fast-growing development of high-performance computers. This thesis investigates novel algorithms for automatic classification of colorectal and prostate cancer using multispectral imagery in order to propose a system outperforming the state-of-the-art techniques in the field. To achieve this objective, several feature extraction methods based on image texture have been investigated, analysed and evaluated. A novel texture feature for multispectral images is also constructed as an adaptation of the local binary pattern texture feature to multispectral images by expanding the pixels neighbourhood to the spectral dimension. It has the advantage of capturing the multispectral information with a limited feature vector size. This feature has demonstrated improved classification results when compared against traditional texture features. In order to further enhance the systems performance, advanced classification schemes such as bag-of-features - to better capture local information - and stacked generalisation - to select the most discriminative texture features - are explored and evaluated. Finally, the recent years have seen an accelerated and exponential rise of deep learning, boosted by the advances in hardware, and more specifically graphics processing units. Such models have demonstrated excellent results for supervised learning in multiple applications. This observation has motivated the employment in this thesis of deep neural network architectures, namely convolutional neural networks. Experiments were also carried out to evaluate and compare the performance obtained with the features extracted using convolutional neural networks with random initialisation against features extracted with pre-trained models on ImageNet dataset. The analysis of the classication accuracy achieved with deep learning models reveals that the latter outperforms the previously proposed texture extraction methods. In this thesis, the algorithms are assessed using two separate multiclass datasets: the first one consists of prostate tumour multispectral images, and the second contains multispectral images of colorectal tumours. The colorectal dataset was acquired on a wide domain of the light spectrum ranging from the visible to the infrared wavelengths. This dataset was used to demonstrate the improved results produced using infrared light as well as visible light

Increasing Accuracy Performance through Optimal Feature Extraction Algorithms

This research developed models and techniques to improve the three key modules of popular recognition systems: preprocessing, feature extraction, and classification. Improvements were made in four key areas: processing speed, algorithm complexity, storage space, and accuracy. The focus was on the application areas of the face, traffic sign, and speaker recognition. In the preprocessing module of facial and traffic sign recognition, improvements were made through the utilization of grayscaling and anisotropic diffusion. In the feature extraction module, improvements were made in two different ways; first, through the use of mixed transforms and second through a convolutional neural network (CNN) that best fits specific datasets. The mixed transform system consists of various combinations of the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), which have a reliable track record for image feature extraction. In terms of the proposed CNN, a neuroevolution system was used to determine the characteristics and layout of a CNN to best extract image features for particular datasets. In the speaker recognition system, the improvement to the feature extraction module comprised of a quantized spectral covariance matrix and a two-dimensional Principal Component Analysis (2DPCA) function. In the classification module, enhancements were made in visual recognition through the use of two neural networks: the multilayer sigmoid and convolutional neural network. Results show that the proposed improvements in the three modules led to an increase in accuracy as well as reduced algorithmic complexity, with corresponding reductions in storage space and processing time