Robust Facial Expression Recognition Using Local Binary Patterns and Gabor Filters

Facial expressions and gestures provide intuitional cues for interpersonal communication. Imparting intelligence to computer for identifying facial expressions is a crucial task. Facial expressions and emotions are governed by identification of facial muscle movement by visual cortex and training a machine to identify these highly in-situ movements is our primary interest. This thesis presents robust facial expression analysis algorithms for static images as well as an efficient extension to sequence of images. We present an efficient preprocessing method which eliminates the effect of illumination on the detected face images thus making them efficient for feature extraction. Robust Local Binary Patterns and Gabor filters are implemented for feature extraction which are known to provide efficient face representation and analysis.LBP facial features are represented in form of weighted histograms which are best classified using Kullback Leibler divergence measure .Artificial Neural Network classifier is also tested for classification of fused Gabor and LBP features. Further expressions are rarely defined by static images as their complete essence lies in a sequence of images. So further exploration is concentrated on analyzing expressions from a sequence of images. To eliminate head pose variations in consecutive frames and register images to keep the spatial information intact which is necessary for LBP feature representation we adopted SIFT flow alignment procedure and further tested the resultant image classification with implemented algorithms. The classification accuracy resulted in 95.24% for static expression images and 86.31% for sequence of images which is indeed appreciable when compared to other standard methods

Revisiting the two predominant statistical problems: the stopping-rule problem and the catch-all hypothesis problem

The history of statistics is filled with many controversies, in which the prime focus has been the difference in the “interpretation of probability” between Fre- quentist and Bayesian theories. Many philosophical arguments have been elabo- rated to examine the problems of both theories based on this dichotomized view of statistics, including the well-known stopping-rule problem and the catch-all hy- pothesis problem. However, there are also several “hybrid” approaches in theory, practice, and philosophical analysis. This poses many fundamental questions. This paper reviews three cases and argues that the interpretation problem of probabil- ity is insufficient to begin a philosophical analysis of the current issues in the field of statistics. A novel viewpoint is proposed to examine the relationship between the stopping-rule problem and the catch-all hypothesis problem

Robust Facial Expression Recognition Using Local Binary Patterns and Gabor Filters

Facial expressions and gestures provide intuitional cues for interpersonal communication. Imparting intelligence to computer for identifying facial expressions is a crucial task. Facial expressions and emotions are governed by identification of facial muscle movement by visual cortex and training a machine to identify these highly in-situ movements is our primary interest. This thesis presents robust facial expression analysis algorithms for static images as well as an efficient extension to sequence of images. We present an efficient preprocessing method which eliminates the effect of illumination on the detected face images thus making them efficient for feature extraction. Robust Local Binary Patterns and Gabor filters are implemented for feature extraction which are known to provide efficient face representation and analysis.LBP facial features are represented in form of weighted histograms which are best classified using Kullback Leibler divergence measure .Artificial Neural Network classifier is also tested for classification of fused Gabor and LBP features. Further expressions are rarely defined by static images as their complete essence lies in a sequence of images. So further exploration is concentrated on analyzing expressions from a sequence of images. To eliminate head pose variations in consecutive frames and register images to keep the spatial information intact which is necessary for LBP feature representation we adopted SIFT flow alignment procedure and further tested the resultant image classification with implemented algorithms. The classification accuracy resulted in 95.24% for static expression images and 86.31% for sequence of images which is indeed appreciable when compared to other standard methods

Information Processing Equalities and the Information-Risk Bridge

We introduce two new classes of measures of information for statistical experiments which generalise and subsume $\phi$-divergences, integral probability metrics, $\mathfrak{N}$-distances (MMD), and $(f,\Gamma)$ divergences between two or more distributions. This enables us to derive a simple geometrical relationship between measures of information and the Bayes risk of a statistical decision problem, thus extending the variational $\phi$-divergence representation to multiple distributions in an entirely symmetric manner. The new families of divergence are closed under the action of Markov operators which yields an information processing equality which is a refinement and generalisation of the classical data processing inequality. This equality gives insight into the significance of the choice of the hypothesis class in classical risk minimization.Comment: 48 pages; corrected some typos and added a few additional explanation

An Unsupervised Cluster: Learning Water Customer Behavior Using Variation of Information on a Reconstructed Phase Space

The unsupervised clustering algorithm described in this dissertation addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into ‘normal’ and ‘unusual’ customer behavior patterns. This research focuses upon individually metered water utility customers and includes both residential and commercial customer accounts serviced by utilities within North America. The contributions of this dissertation not only represent a novel academic work, but also solve a practical problem for the utility industry. This dissertation introduces a method of agglomerative clustering using information theoretic distance measures on Gaussian mixture models within a reconstructed phase space. The clustering method accommodates a utility’s limited human, financial, computational, and environmental resources. The proposed weighted variation of information distance measure for comparing Gaussian mixture models places emphasis upon those behaviors whose statistical distributions are more compact over those behaviors with large variation and contributes a novel addition to existing comparison options

Deep Face Morph Detection Based on Wavelet Decomposition

Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this dissertation, we delve into data-driven morph detection which is of great significance in terms of national security. We propose several wavelet-based face morph detection schemes which employ some of the computer vision algorithms such as image wavelet analysis, group sparsity, feature selection, and the visual attention mechanisms. Wavelet decomposition enables us to leverage the fine-grained frequency content of an image to boost localizing manipulated areas in an image. Our methodologies are as follows: (1) entropy-based single morph detection, (2) entropy-based differential morph detection, (3) morph detection using group sparsity, and (4) Attention aware morph detection. In the first methodology, we harness mismatches between the entropy distribution of wavelet subbands corresponding to a pair of real and morph images to find a subset of most discriminative wavelet subbands which leads to an increase of morph detection accuracy. As the second methodology, we adopt entropy-based subband selection to tackle differential morph detection. In the third methodology, group sparsity is leveraged for subband selection. In other words, adding a group sparsity constraint to the loss function of our DNN leads to an implicit subband selection. Our fourth methodology consists of different types of visual attention mechanisms such as convolutional block attention modules and self-attention resulting in boosting morph detection accuracy. We demonstrate efficiency of our proposed algorithms through several morph datasets via extensive evaluations as well as visualization methodologies

Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the representations that are derived from the data in the learning process. The representations learned in a successful learning process should be concise, discrete, meaningful, and able to be applied across a variety of tasks. A recent effort has been directed toward developing Deep Learning models, which have proven to be particularly effective at capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we discuss the principles and developments that have been made in the process of learning representations, and converting them into desirable applications. In addition, for each framework or model, the key issues and open challenges, as well as the advantages, are examined