78 research outputs found
Local feature extraction based facial emotion recognition: a survey
Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively
Personal verification based on multi-spectral finger texture lighting images
Finger Texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the Surrounded Patterns Code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. The SPC approach proposes using a single texture descriptor for FT images captured under multispectral illuminations, where this reduces the cost of employing different feature extraction methods for different spectral FT images. Furthermore, a novel classifier termed the Re-enforced Probabilistic Neural Network (RPNN) is proposed. It enhances
the capability of the standard Probabilistic Neural Network (PNN) and provides better recognition performance. Two types of FT images from the Multi-Spectral CASIA (MSCASIA) database were employed as two types of spectral sensors were used in the acquiring device: the White (WHT) light and spectral 460 nm of Blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the Equal Error Rates (EERs) at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilizing the RPNN
Tensor Representations for Object Classification and Detection
A key problem in object recognition is finding a suitable object representation.
For historical and computational reasons, vector descriptions that encode particular
statistical properties of the data have been broadly applied. However, employing
tensor representation can describe the interactions of multiple factors
inherent to image formation. One of the most convenient uses for tensors is to represent
complex objects in order to build a discriminative description.
Thus thesis has several main contributions, focusing on visual data detection (e.g. of heads or pedestrians) and classification (e.g. of head or human body orientation) in still images and on machine learning techniques to analyse tensor data. These applications are among the most studied in computer vision and are typically formulated as binary or multi-class classification problems.
The applicative context of this thesis is the video surveillance, where classification and detection tasks
can be very hard, due to the scarce resolution and the noise characterising
sensor data. Therefore, the main goal in that context is to design algorithms that can
characterise different objects of interest, especially when immersed in a cluttered
background and captured at low resolution.
In the different amount of machine learning approaches, the ensemble-of-classifiers demonstrated to reach
excellent classification accuracy, good generalisation ability, and robustness of noisy data. For these
reasons, some approaches in that class have been adopted as basic machine classification
frameworks to build robust classifiers and detectors. Moreover, also
kernel machines has been exploited for classification purposes,
since they represent a natural learning framework for tensors
Toward Understanding Human Expression in Human-Robot Interaction
Intelligent devices are quickly becoming necessities to support our activities during both work and play. We are already bound in a symbiotic relationship with these devices. An unfortunate effect of the pervasiveness of intelligent devices is the substantial investment of our time and effort to communicate intent. Even though our increasing reliance on these intelligent devices is inevitable, the limits of conventional methods for devices to perceive human expression hinders communication efficiency. These constraints restrict the usefulness of intelligent devices to support our activities. Our communication time and effort must be minimized to leverage the benefits of intelligent devices and seamlessly integrate them into society. Minimizing the time and effort needed to communicate our intent will allow us to concentrate on tasks in which we excel, including creative thought and problem solving. An intuitive method to minimize human communication effort with intelligent devices is to take advantage of our existing interpersonal communication experience. Recent advances in speech, hand gesture, and facial expression recognition provide alternate viable modes of communication that are more natural than conventional tactile interfaces. Use of natural human communication eliminates the need to adapt and invest time and effort using less intuitive techniques required for traditional keyboard and mouse based interfaces. Although the state of the art in natural but isolated modes of communication achieves impressive results, significant hurdles must be conquered before communication with devices in our daily lives will feel natural and effortless. Research has shown that combining information between multiple noise-prone modalities improves accuracy. Leveraging this complementary and redundant content will improve communication robustness and relax current unimodal limitations. This research presents and evaluates a novel multimodal framework to help reduce the total human effort and time required to communicate with intelligent devices. This reduction is realized by determining human intent using a knowledge-based architecture that combines and leverages conflicting information available across multiple natural communication modes and modalities. The effectiveness of this approach is demonstrated using dynamic hand gestures and simple facial expressions characterizing basic emotions. It is important to note that the framework is not restricted to these two forms of communication. The framework presented in this research provides the flexibility necessary to include additional or alternate modalities and channels of information in future research, including improving the robustness of speech understanding. The primary contributions of this research include the leveraging of conflicts in a closed-loop multimodal framework, explicit use of uncertainty in knowledge representation and reasoning across multiple modalities, and a flexible approach for leveraging domain specific knowledge to help understand multimodal human expression. Experiments using a manually defined knowledge base demonstrate an improved average accuracy of individual concepts and an improved average accuracy of overall intents when leveraging conflicts as compared to an open-loop approach
Face Recognition from Face Signatures
This thesis presents techniques for detecting and recognizing faces under various
imaging conditions. In particular, it presents a system that combines several
methods for face detection and recognition. Initially, the faces in the images are
located using the Viola-Jones method and each detected face is represented by
a subimage. Then, an eye and mouth detection method is used to identify the
coordinates of the eyes and mouth, which are then used to update the subimages
so that the subimages contain only the face area. After that, a method based
on Bayesian estimation and a fuzzy membership function is used to identify the
actual faces on both subimages (obtained from the first and second steps). Then, a
face similarity measure is used to locate the oval shape of a face in both subimages.
The similarity measures between the two faces are compared and the one with
the highest value is selected.
In the recognition task, the Trace transform method is used to extract the
face signatures from the oval shape face. These signatures are evaluated using
the BANCA and FERET databases in authentication tasks. Here, the signatures
with discriminating ability are selected and were used to construct a classifier.
However, the classifier was shown to be a weak classifier. This problem is
tackled by constructing a boosted assembly of classifiers developed by a Gentle
Adaboost algorithm. The proposed methodologies are evaluated using a family
album database
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Nonconvex Recovery of Low-complexity Models
Today we are living in the era of big data, there is a pressing need for efficient, scalable and robust optimization methods to analyze the data we create and collect. Although Convex methods offer tractable solutions with global optimality, heuristic nonconvex methods are often more attractive in practice due to their superior efficiency and scalability. Moreover, for better representations of the data, the mathematical model we are building today are much more complicated, which often results in highly nonlinear and nonconvex optimizations problems. Both of these challenges require us to go beyond convex optimization. While nonconvex optimization is extraordinarily successful in practice, unlike convex optimization, guaranteeing the correctness of nonconvex methods is notoriously difficult. In theory, even finding a local minimum of a general nonconvex function is NP-hard – nevermind the global minimum.
This thesis aims to bridge the gap between practice and theory of nonconvex optimization, by developing global optimality guarantees for nonconvex problems arising in real-world engineering applications, and provable, efficient nonconvex optimization algorithms. First, this thesis reveals that for certain nonconvex problems we can construct a model specialized initialization that is close to the optimal solution, so that simple and efficient methods provably converge to the global solution with linear rate. These problem include sparse basis learning and convolutional phase retrieval. In addition, the work has led to the discovery of a broader class of nonconvex problems – the so-called ridable saddle functions. Those problems possess characteristic structures, in which (i) all local minima are global, (ii) the energy landscape does not have any ''flat'' saddle points. More interestingly, when data are large and random, this thesis reveals that many problems in the real world are indeed ridable saddle, those problems include complete dictionary learning and generalized phase retrieval. For each of the aforementioned problems, the benign geometric structure allows us to obtain global recovery guarantees by using efficient optimization methods with arbitrary initialization
Investigating the Role of ATRX in Glutamatergic Hippocampal Neurons
Mutations in ATRX, a Snf2-type chromatin remodeler, frequently lead to intellectual disability. However, the function of ATRX within the brain in cognition and synaptic transmission are incompletely understood. The aim of this study was to investigate the role of ATRX in the adult mouse brain. While complete loss of ATRX in the embryonic mouse brain results in perinatal lethality, mosaic expression of ATRX stunted growth and perturbed circulating IGF-1 levels. Mosaic expression of ATRX also impaired adult cognition, specifically recognition memory and spatial learning and memory. However, there were confounding factors that led me to a new model in which I deleted the gene in postnatal mouse glutamatergic neurons. Magnetic resonance imaging of these mice revealed increased hippocampal CA1 and CA3 layers, and behaviour analysis indicated deficiencies in hippocampal-dependent learning and memory in the contextual fear task, Morris water maze, and paired-associate learning task. These behavioural abnormalities were not present in the female counterparts. Transmission electron microscopy of male hippocampal CA1 synapses revealed decreased number of total and docked vesicles and increased cleft width and post-synaptic density size. Hippocampal RNA-sequencing followed by sex-interaction analysis of male and female knockout transcripts highlighted potential impairments in the synaptic vesicle cycle. miR-137, a known regulator of presynaptic vesicle cycle and plasticity, was upregulated in the male knockout hippocampi but downregulated in the female knockouts. These results demonstrate sexually-dimorphic regulation of miR-137 and learning and memory by ATRX in forebrain glutamatergic neurons, indicating potential miRNA-targeting therapies for cognitive disorders by ATRX mutations
ALTERED EXPRESSION AND FUNCTIONALITY OF A2A ADENOSINE RECEPTORS IN HUNTINGTON’S DISEASE AND OTHER POLYGLUTAMINE DISORDERS
Several studies have suggested the possible involvement of A2A adenosine receptors in the
pathogenesis of neuronal disorders, including Huntington’s disease. Huntington’s disease is an
inherited neurodegenerative disease clinically characterized by motor, cognitive and behavioural
impairments. The genetic cause of the disease is the expanded CAG triplet in a gene coding for
huntingtin, a protein involved in several physiological processes. Huntington’s disease affects
primarly GABAergic neurons in the basal ganglia that express adenosine A2A and dopamine D2
receptors. The present study describes a functional alteration of A2A adenosine receptor in striatal
cells engineerized to express full length or truncated, wild type or mutant huntingtin. The data
obtained demonstrate that the presence of mutant huntingtin induce an amplification of the
transduction signal mediated by adenylyl cyclase and an aberrant coupling of A2A receptor to this
transduction pathway. The expression and functionality of A2A adenosine receptor were
subsequently evaluated in transgenic mice R6/2, an animal model of Huntington’s disease that
express exon 1 of the human huntingtin gene. Saturation binding experiments revealed an increase
of A2A receptor levels in striatum of R6/2 mice until 14 post natal days. In addition, also the potency
of a typical A2A agonist was increased in striatal membranes of R6/2 mice when compared to wild
type mice. The subsequent study aimed the evaluation of the presence and functionality of A2A
adenosine receptors in peripheral blood cells from patients affected by Huntington’s disease
compared with control subjects. The results revealed a statistically significant increase of the A2A
receptor density in platelets, lymphocytes and neutrophils of Huntington’s disease patients and presymptomatic
carriers of the mutation when compared to control subjects. In order to verify the
specificity of A2A receptor alteration in polyglutamine disease, the same study was conducted in
blood cells from patients affected by Spinocerebellar ataxia, characterized by an expanded CAG
triplet in the ataxin gene and in patients affected by Friedreich’s ataxia, characterized by an
expansion of the GAA triplet. Saturation binding experiments in peripheral blood cells from
Spinocerebellar ataxia showed altered A2A binding parameters similar to those obtained in
Huntington’s disease patients. In addition, data obtained in Friedreich’s ataxia patients showed
affinity and density values for A2A receptors similar to those obtained from control subjects,
demonstrating the involvement of the CAG but not of the GAA triplet. Overall these data
demonstrate that an aberrant A2A receptor phenotype is present in polyglutamine disorders and this
seems to be related with the expanded CAG triplet. The amplification of the signal transduction
system of A2A receptors suggests that the use of selective A2A antagonists could be beneficial in the
treatment of Huntington’s disease as well as in other related polyglutamine diseases. In addition, the
alteration of A2A receptors in peripheral blood cells of patients with polyglutamine diseases
suggests that this receptor could be an easily accessible biomarker for the evaluation of the efficacy
of potential new therapies
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