A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods

Convex and non-convex optimization using centroid-encoding for visualization, classification, and feature selection

Includes bibliographical references.2022 Fall.Classification, visualization, and feature selection are the three essential tasks of machine learning. This Ph.D. dissertation presents convex and non-convex models suitable for these three tasks. We propose Centroid-Encoder (CE), an autoencoder-based supervised tool for visualizing complex and potentially large, e.g., SUSY with 5 million samples and high-dimensional datasets, e.g., GSE73072 clinical challenge data. Unlike an autoencoder, which maps a point to itself, a centroid-encoder has a modified target, i.e., the class centroid in the ambient space. We present a detailed comparative analysis of the method using various data sets and state-of-the-art techniques. We have proposed a variation of the centroid-encoder, Bottleneck Centroid-Encoder (BCE), where additional constraints are imposed at the bottleneck layer to improve generalization performance in the reduced space. We further developed a sparse optimization problem for the non-linear mapping of the centroid-encoder called Sparse Centroid-Encoder (SCE) to determine the set of discriminate features between two or more classes. The sparse model selects variables using the 1-norm applied to the input feature space. SCE extracts discriminative features from multi-modal data sets, i.e., data whose classes appear to have multiple clusters, by using several centers per class. This approach seems to have advantages over models which use a one-hot-encoding vector. We also provide a feature selection framework that first ranks each feature by its occurrence, and the optimal number of features is chosen using a validation set. CE and SCE are models based on neural network architectures and require the solution of non-convex optimization problems. Motivated by the CE algorithm, we have developed a convex optimization for the supervised dimensionality reduction technique called Centroid Component Retrieval (CCR). The CCR model optimizes a multi-objective cost by balancing two complementary terms. The first term pulls the samples of a class towards its centroid by minimizing a sample's distance from its class centroid in low dimensional space. The second term pushes the classes by maximizing the scattering volume of the ellipsoid formed by the class-centroids in embedded space. Although the design principle of CCR is similar to LDA, our experimental results show that CCR exhibits performance advantages over LDA, especially on high-dimensional data sets, e.g., Yale Faces, ORL, and COIL20. Finally, we present a linear formulation of Centroid-Encoder with orthogonality constraints, called Principal Centroid Component Analysis (PCCA). This formulation is similar to PCA, except the class labels are used to formulate the objective, resulting in the form of supervised PCA. We show the classification and visualization experiments results with this new linear tool

Unsupervised deep learning research and implementation of variational autoencoders

Generative models have been one of the major research fields in unsupervised deep learning during the last years. They are achieving promising results in learning the distribution of multidimensional variables as well as in finding meaningful hidden representations in data. The aim of this thesis is to gain a sound understanding of generative models through a profound study of one of the most promising and widely used generative models family, the variational autoencoders. In particular, the performance of the standard variational autoencoder (known as VAE) and the Gaussian Mixture variational autoencoder (called GMVAE) is assessed. First, the mathematical and probabilistic basis of both models is presented. Then, the models are implemented in Python using the Tensorflow framework. The source code is freely available and documented in a personal GitHub repository created for this thesis. Later, the performance of the implemented models is appraised in terms of generative capabilities and interpretability of the hidden representation of the inputs. Two real datasets are used during the experiments, the MNIST and "Frey faces". Results show the models implemented work correctly, and they also show the GMVAE outweighs the performance of the standard VAE, as expected.Ingeniería en Tecnologías de Telecomunicació

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

The computational face for facial emotion analysis: Computer based emotion analysis from the face

Facial expressions are considered to be the most revealing way of understanding the human psychological state during face-to-face communication. It is believed that a more natural interaction between humans and machines can be undertaken through the detailed understanding of the different facial expressions which imitate the manner by which humans communicate with each other. In this research, we study the different aspects of facial emotion detection, analysis and investigate possible hidden identity clues within the facial expressions. We study a deeper aspect of facial expressions whereby we try to identify gender and human identity - which can be considered as a form of emotional biometric - using only the dynamic characteristics of the smile expressions. Further, we present a statistical model for analysing the relationship between facial features and Duchenne (real) and non-Duchenne (posed) smiles. Thus, we identify that the expressions in the eyes contain discriminating features between Duchenne and non-Duchenne smiles. Our results indicate that facial expressions can be identified through facial movement analysis models where we get an accuracy rate of 86% for classifying the six universal facial expressions and 94% for classifying the common 18 facial action units. Further, we successfully identify the gender using only the dynamic characteristics of the smile expression whereby we obtain an 86% classification rate. Likewise, we present a framework to study the possibility of using the smile as a biometric whereby we show that the human smile is unique and stable.Al-Zaytoonah Universit

Electroencephalographic event-related potentials during social interactions in people with symptoms of major depression and social anxiety

Social interactions are severely impaired in major depression and social anxiety. Social interactions associated with fairness have been studied using the Ultimatum Game (UG). In the UG, the responder receives an offer from a proposer about how to divide a sum of money. If the responder accepts the offer, both accumulate the money. If the responder rejects the offer, both accumulates nothing. The UG is comprised by fair (the responder is offered between 40-50% of the total amount of the offer), medium (the responder is offered between 27-33%) and unfair offers (the responder is offered between 18-23%). Previous studies have reported that at least two event-related potentials (ERPs) are modulated by fairness during the UG: the Medial Frontal Negativity (MFN) and the Late Positive Potential/P300 (LPP/P300). To our knowledge, there are no studies analysing ERPs in the UG in major depression and/or social anxiety. We aimed to study the MFN, the LPP/P300, the behavior and the emotions experienced during the UG in healthy volunteers (Control group, n = 72) and volunteers with symptoms of major depression and/or social anxiety (MD/SA group, n = 63). As expected, we found that the rejection of offers increased as the unfairness increased. Also, the medium offers were associated with longer reaction times than unfair offers, and in turn unfair offers were associated with longer reaction times than fair offers. In addition, participants reported less positive emotions and more negative emotions as the unfairness increased. Interestingly, in comparison with the Control group, the MD/SA group reported feeling more sadness in all offers, and specially in medium and unfair offers. The MFN was associated with more negative mean amplitudes in medium and unfair offers than in fair offers. This component would be modulated by the negative emotional/motivational impact caused by unfairness. Another plausible interpretation is that the volunteers would expect fair offers during the task, and that the unexpected medium and unfair offers would be associated with error predictions that would evoke the MFN. Interestingly, our results showed a borderline main effect of group in the MFN, suggesting that this component was associated with more negative mean amplitudes in the MD/SA group in comparison with the Control group, across all offers. This effect would be associated with enhanced negative emotions experienced during all levels of fairness during the UG in the MD/SA group, in comparison to controls. In addition, the LPP/P300 was associated with more positive mean amplitudes in fair offers in comparison with unfair offers, and with more positive mean amplitudes in unfair offers in comparison with medium offers. This result would indicate that fair offers would be associated with more arousal than unfair and medium offers, and that in turn unfair offers would be associated with more arousal than medium offers. An alternative interpretation is that the LPP/P300 would be modulated by the conflict associated with the decision-making of each level of fairness. In this line, the LPP/P300 would be more positive in fair, less conflictive offers in comparison with unfair and medium offers. In turn, this component would be more positive in unfair offers in comparison with medium offers, given that the medium offers are the most conflicting. Of note, the LPP/P300 was associated with more negative mean amplitudes in the MD/SA group in comparison with the Control group across all offers. This result could be related to less arousal and/or motivation in the MD/SA group in comparison with the Control group. Alternatively, this effect could be related to less attentional resources available to allocate to the task in the MD/SA group in comparison with controls. The lack of attentional resources in the MD/SA group could be due to the activation of other cognitive processes, such as rumination, which would compete for the cognitive resources available. Another interpretation is that this effect could be related to higher levels of conflict experienced during all offers by the MD/SA group. We aim to contribute to the understanding of social functioning and the neural basis associated with these processes in major depression and social anxiety.Las interacciones sociales se encuentran severamente afectadas en la depresión mayor y en la ansiedad social. Ciertos procesos relacionados a interacciones sociales justas e injustas se han estudiado utilizando el Ultimatum Game (UG). Durante el UG, el responder recibe ofertas de un proposer acerca de cómo dividir una cantidad de dinero. Si el responder acepta la oferta, ambos acumulan el dinero. Si el responder rechaza la oferta, ninguno acumulan nada. Durante el UG hay ofertas justas (al responder le ofrecen entre el 40-50% del total), medias (le ofrecen 27-33%) e injustas (le ofrecen 18-23%). Se ha reportado que al menos dos potenciales relacionados a eventos (ERPs, por la sigla en inglés para Event-Related Potentials) son modulados por la justicia durante el UG: el Medial Frontal Negativity (MFN) y el Late Positive Potential/P300 (LPP/P300). A nuestro conocimiento, no hay estudios que analicen ERPs durante el UG en depresión mayor y/o ansiedad social. Este proyecto tuvo como objetivo estudiar el MFN, el LPP/P300, el comportamiento y las emociones experimentadas durante el UG en personas sin historia de trastornos mentales (grupo Control, n = 72) y personas con síntomas de depresión mayor y/o ansiedad social (grupo MD/SA, n = 63). Como era esperado, la tasa de rechazo aumentó a medida que aumentaba la injusticia. Además, los tiempos de reacción fueron mayores en las ofertas medias en comparación a las injustas, y mayores en las ofertas injustas en comparación a las justas. Asimismo, los participantes reportaron más emociones negativas y menos emociones positivas a medida que aumentaba la injusticia. Cabe señalar que el grupo MD/SA reportó mayores niveles de tristeza en todas las ofertas, y especialmente en las ofertas medias e injustas. Además, el MFN se asoció a una amplitud media más negativa en las ofertas medias e injustas en comparación a las justas, lo que reflejaría el impacto emocional/motivacional negativo causado por la injusticia. Otra explicación posible es que los voluntarios esperarían recibir ofertas justas durante el UG, y que las ofertas medias e injustas inesperadas se asociarían a un error de predicción que modularía el MFN. Cabe destacar que se encontró una tendencia en el efecto principal de grupo en el MFN, sugiriendo que, en todas las ofertas, el MFN estaría asociado a una amplitud media más negativa en el grupo MD/SA en comparación al grupo Control. Este efecto estaría relacionado con emociones negativas acentuadas durante todas las ofertas en el grupo MD/SA en comparación al grupo Control. Asimismo, el LPP/P300 se asoció a una amplitud media más positiva durante ofertas justas en comparación a injustas, y con una amplitud media más positiva durante ofertas injustas en comparación a medias. Este resultado indicaría que las ofertas justas se asociarían con mayores niveles de arousal en comparación a las ofertas injustas y medias, y que las ofertas injustas se asociarían a mayores niveles de arousal que las ofertas medias. Una explicación alternativa es que el LPP/P300 sería modulado por el nivel de conflicto en la toma de decisiones. En este sentido, el LPP/P300 aparecería más positivo en las ofertas justas, que son las menos conflictivas, en comparación a ofertas injustas y medias. A su vez, el LPP/P300 se asociaría a amplitudes medias más negativas en las ofertas medias, dado que son las más conflictivas, en comparación a justas e injustas. Cabe resaltar que el LPP/P300 se asoció con una amplitud media más negativa en el grupo MD/SA que en el grupo Control en todas las ofertas. Este resultado podría estar asociado a menores niveles de arousal y/o motivación en el grupo MD/SA en comparación al grupo Control. Alernativamente, este efecto podría estar asociado a menores recursos atencionales disponibles para asignar a la tarea en el grupo MD/SA en comparación al grupo Control. Esto podría deberse a la actividad de otros procesos cognitivos que consumirían recursos cognitivos, como por ejemplo, rumia. Otra interpretación es que este efecto podría deberse a mayores niveles de conflicto experimentado en todas las ofertas en el grupo MD/SA. Esperamos contribuir al entendimiento del funcionamiento social y de las bases neurales asociadas a estos procesos en la depresión mayor y la ansiedad social

Statistical Model Evaluation Using Reproducing Kernels and Stein’s method

Advances in computing have enabled us to develop increasingly complex statistical models. However, their complexity poses challenges in their evaluation. The central theme of the thesis is addressing intractability and interpretability in model evaluations. The key tools considered in the thesis are kernel and Stein's methods: Kernel methods provide flexible means of specifying features for comparing models, and Stein's method further allows us to incorporate model structures in evaluation. The first part of the thesis addresses the question of intractability. The focus is on latent variable models, a large class of models used in practice, including factor models, topic models for text, and hidden Markov models. The kernel Stein discrepancy (KSD), a kernel-based discrepancy, is extended to deal with this model class. Based on this extension, a statistical hypothesis test of relative goodness of fit is developed, enabling us to compare competing latent variable models that are known up to normalization. The second part of the thesis concerns the question of interpretability with two contributed works. First, interpretable relative goodness-of-fit tests are developed using kernel-based discrepancies developed in Chwialkowski et al. (2015); Jitkrittum et al. (2016); Jitkrittum et al. (2017). These tests allow the user to choose features for comparison and discover aspects distinguishing two models. Second, a convergence property of the KSD is established. Specifically, the KSD is shown to control an integral probability metric defined by a class of polynomially growing continuous functions. In particular, this development allows us to evaluate both unnormalized statistical models and sample approximations to posterior distributions in terms of moments