Novel image descriptors and learning methods for image classification applications

Image classification is an active and rapidly expanding research area in computer vision and machine learning due to its broad applications. With the advent of big data, the need for robust image descriptors and learning methods to process a large number of images for different kinds of visual applications has greatly increased. Towards that end, this dissertation focuses on exploring new image descriptors and learning methods by incorporating important visual aspects and enhancing the feature representation in the discriminative space for advancing image classification. First, an innovative sparse representation model using the complete marginal Fisher analysis (CMFA-SR) framework is proposed for improving the image classification performance. In particular, the complete marginal Fisher analysis method extracts the discriminatory features in both the column space of the local samples based within class scatter matrix and the null space of its transformed matrix. To further improve the classification capability, a discriminative sparse representation model is proposed by integrating a representation criterion such as the sparse representation and a discriminative criterion. Second, the discriminative dictionary distribution based sparse coding (DDSC) method is presented that utilizes both the discriminative and generative information to enhance the feature representation. Specifically, the dictionary distribution criterion reveals the class conditional probability of each dictionary item by using the dictionary distribution coefficients, and the discriminative criterion applies new within-class and between-class scatter matrices for discriminant analysis. Third, a fused color Fisher vector (FCFV) feature is developed by integrating the most expressive features of the DAISY Fisher vector (D-FV) feature, the WLD-SIFT Fisher vector (WS-FV) feature, and the SIFT-FV feature in different color spaces to capture the local, color, spatial, relative intensity, as well as the gradient orientation information. Furthermore, a sparse kernel manifold learner (SKML) method is applied to the FCFV features for learning a discriminative sparse representation by considering the local manifold structure and the label information based on the marginal Fisher criterion. Finally, a novel multiple anthropological Fisher kernel framework (M-AFK) is presented to extract and enhance the facial genetic features for kinship verification. The proposed method is derived by applying a novel similarity enhancement approach based on SIFT flow and learning an inheritable transformation on the multiple Fisher vector features that uses the criterion of minimizing the distance among the kinship samples and maximizing the distance among the non-kinship samples. The effectiveness of the proposed methods is assessed on numerous image classification tasks, such as face recognition, kinship verification, scene classification, object classification, and computational fine art painting categorization. The experimental results on popular image datasets show the feasibility of the proposed methods

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

CELL PATTERN CLASSIFICATION OF INDIRECT IMMUNOFLUORESCENCE IMAGES

Ph.DDOCTOR OF PHILOSOPH

Reconnaissance des expressions faciales pour l’assistance ambiante

Au cours de ces dernières décennies, le monde a connu d’importants changements démographiques et notamment au niveau de la population âgée qui a fortement augmenté. La prise d’âge a comme conséquence directe non seulement une perte progressive des facultés cognitives, mais aussi un risque plus élevé d’être atteint de maladies neurodégénératives telles qu’Alzheimer et Parkinson. La perte des facultés cognitives cause une diminution de l’autonomie et par conséquent, une assistance quotidienne doit être fournie à ces individus afin d’assurer leur bien-être. Les établissements ainsi que le personnel spécialisé censés les prendre en charge représentent un lourd fardeau pour l’économie. Pour cette raison, d’autres solutions moins coûteuses et plus optimisées doivent être proposées. Avec l’avènement des nouvelles technologies de l’information et de la communication, il est devenu de plus en plus aisé de développer des solutions permettant de fournir une assistance adéquate aux personnes souffrant de déficiences cognitives. Les maisons intelligentes représentent l’une des solutions les plus répandues. Elles exploitent différents types de capteurs pour la collecte de données, des algorithmes et méthodes d’apprentissage automatique pour l’extraction/traitement de l’information et des actionneurs pour le déclenchement d’une réponse fournissant une assistance adéquate. Parmi les différentes sources de données qui sont exploitées, les images/vidéos restent les plus riches en termes de quantité. Les données récoltées permettent non seulement la reconnaissance d’activités, mais aussi la détection d’erreur durant l’exécution de tâches/activités de la vie quotidienne. La reconnaissance automatique des émotions trouve de nombreuses applications dans notre vie quotidienne telles que l’interaction homme-machine, l’éducation, la sécurité, le divertissement, la vision robotique et l’assistance ambiante. Cependant, les émotions restent un sujet assez complexe à cerner et de nombreuses études en psychologie et sciences cognitives continuent d’être effectuées. Les résultats obtenus servent de base afin de développer des approches plus efficaces. Les émotions humaines peuvent être perçues à travers différentes modalités telle que la voix, la posture, la gestuelle et les expressions faciales. En se basant sur les travaux de Mehrabian, les expressions faciales représentent la modalité la plus pertinente pour la reconnaissance automatique des émotions. Ainsi, l’un des objectifs de ce travail de recherche consistera à proposer des méthodes permettant l’identification des six émotions de base à savoir : la joie, la peur, la colère, la surprise, le dégoût et la tristesse. Les méthodes proposées exploitent des données d’entrée statiques et dynamiques, elles se basent aussi sur différents types de descripteurs/représentations (géométrique, apparence et hybride). Après avoir évalué les performances des méthodes proposées avec des bases de données benchmark à savoir : JAFFE, KDEF, RaFD, CK+, MMI et MUG. L’objectif principal de ce travail de recherche réside dans l’utilisation des expressions faciales afin d’améliorer les performances des systèmes d’assistance existants. Ainsi, des expérimentations ont été conduites au sein de l’environnement intelligent LIARA afin de collecter des données de validation, et ce, en suivant un protocole d’expérimentation spécifique. Lors de l’exécution d’une tâche de la vie quotidienne (préparation du café), deux types de données ont été récoltés. Les données RFID ont permis de valider la méthode de reconnaissance automatique des actions utilisateurs ainsi que la détection automatique d’erreurs. Quant aux données faciales, elles ont permis d’évaluer la contribution des expressions faciales afin d’améliorer les performances du système d’assistance en termes de détection d’erreurs. Avec une réduction du taux de fausses détections dépassant les 20%, l’objectif fixé a été atteint avec succè

Joint optimization of manifold learning and sparse representations for face and gesture analysis

Face and gesture understanding algorithms are powerful enablers in intelligent vision systems for surveillance, security, entertainment, and smart spaces. In the future, complex networks of sensors and cameras may disperse directions to lost tourists, perform directory lookups in the office lobby, or contact the proper authorities in case of an emergency. To be effective, these systems will need to embrace human subtleties while interacting with people in their natural conditions. Computer vision and machine learning techniques have recently become adept at solving face and gesture tasks using posed datasets in controlled conditions. However, spontaneous human behavior under unconstrained conditions, or in the wild, is more complex and is subject to considerable variability from one person to the next. Uncontrolled conditions such as lighting, resolution, noise, occlusions, pose, and temporal variations complicate the matter further. This thesis advances the field of face and gesture analysis by introducing a new machine learning framework based upon dimensionality reduction and sparse representations that is shown to be robust in posed as well as natural conditions. Dimensionality reduction methods take complex objects, such as facial images, and attempt to learn lower dimensional representations embedded in the higher dimensional data. These alternate feature spaces are computationally more efficient and often more discriminative. The performance of various dimensionality reduction methods on geometric and appearance based facial attributes are studied leading to robust facial pose and expression recognition models. The parsimonious nature of sparse representations (SR) has successfully been exploited for the development of highly accurate classifiers for various applications. Despite the successes of SR techniques, large dictionaries and high dimensional data can make these classifiers computationally demanding. Further, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where for example variations in pose may affect identity and expression recognition. This thesis analyzes the interaction between dimensionality reduction and sparse representations to present a unified sparse representation classification framework that addresses both issues of computational complexity and coefficient contamination. Semi-supervised dimensionality reduction is shown to mitigate the coefficient contamination problems associated with SR classifiers. The combination of semi-supervised dimensionality reduction with SR systems forms the cornerstone for a new face and gesture framework called Manifold based Sparse Representations (MSR). MSR is shown to deliver state-of-the-art facial understanding capabilities. To demonstrate the applicability of MSR to new domains, MSR is expanded to include temporal dynamics. The joint optimization of dimensionality reduction and SRs for classification purposes is a relatively new field. The combination of both concepts into a single objective function produce a relation that is neither convex, nor directly solvable. This thesis studies this problem to introduce a new jointly optimized framework. This framework, termed LGE-KSVD, utilizes variants of Linear extension of Graph Embedding (LGE) along with modified K-SVD dictionary learning to jointly learn the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier. By injecting LGE concepts directly into the K-SVD learning procedure, this research removes the support constraints K-SVD imparts on dictionary element discovery. Results are shown for facial recognition, facial expression recognition, human activity analysis, and with the addition of a concept called active difference signatures, delivers robust gesture recognition from Kinect or similar depth cameras

Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring

This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency

Face recognition in video surveillance from a single reference sample through domain adaptation

Face recognition (FR) has received significant attention during the past decades in many applications, such as law enforcement, forensics, access controls, information security and video surveillance (VS), due to its covert and non-intrusive nature. FR systems specialized for VS seek to accurately detect the presence of target individuals of interest over a distributed network of video cameras under uncontrolled capture conditions. Therefore, recognizing faces of target individuals in such environment is a challenging problem because the appearance of faces varies due to changes in pose, scale, illumination, occlusion, blur, etc. The computational complexity is also an important consideration because of the growing number of cameras, and the processing time of state-of-the-art face detection, tracking and matching algorithms. In this thesis, adaptive systems are proposed for accurate still-to-video FR, where a single (or very few) reference still or a mug-shot is available to design a facial model for the target individual. This is a common situation in real-world watch-list screening applications due to the cost and feasibility of capturing reference stills, and managing facial models over time. The limited number of reference stills can adversely affect the robustness of facial models to intra-class variations, and therefore the performance of still-to-video FR systems. Moreover, a specific challenge in still-to-video FR is the shift between the enrollment domain, where high-quality reference faces are captured under controlled conditions from still cameras, and the operational domain, where faces are captured with video cameras under uncontrolled conditions. To overcome the challenges of such single sample per person (SSPP) problems, 3 new systems are proposed for accurate still-to-video FR that are based on multiple face representations and domain adaptation. In particular, this thesis presents 3 contributions. These contributions are described with more details in the following statements. In Chapter 3, a multi-classifier framework is proposed for robust still-to-video FR based on multiple and diverse face representations of a single reference face still. During enrollment of a target individual, the single reference face still is modeled using an ensemble of SVM classifiers based on different patches and face descriptors. Multiple feature extraction techniques are applied to patches isolated in the reference still to generate a diverse SVM pool that provides robustness to common nuisance factors (e.g., variations in illumination and pose). The estimation of discriminant feature subsets, classifier parameters, decision thresholds, and ensemble fusion functions is achieved using the high-quality reference still and a large number of faces captured in lower quality video of non-target individuals in the scene. During operations, the most competent subset of SVMs are dynamically selected according to capture conditions. Finally, a head-face tracker gradually regroups faces captured from different people appearing in a scene, while each individual-specific ensemble performs face matching. The accumulation of matching scores per face track leads to a robust spatio-temporal FR when accumulated ensemble scores surpass a detection threshold. Experimental results obtained with the Chokepoint and COX-S2V datasets show a significant improvement in performance w.r.t. reference systems, especially when individual-specific ensembles (1) are designed using exemplar-SVMs rather than one-class SVMs, and (2) exploit score-level fusion of local SVMs (trained using features extracted from each patch), rather than using either decision-level or feature-level fusion with a global SVM (trained by concatenating features extracted from patches). In Chapter 4, an efficient multi-classifier system (MCS) is proposed for accurate still-to-video FR based on multiple face representations and domain adaptation (DA). An individual-specific ensemble of exemplar-SVM (e-SVM) classifiers is thereby designed to improve robustness to intra-class variations. During enrollment of a target individual, an ensemble is used to model the single reference still, where multiple face descriptors and random feature subspaces allow to generate a diverse pool of patch-wise classifiers. To adapt these ensembles to the operational domains, e-SVMs are trained using labeled face patches extracted from the reference still versus patches extracted from cohort and other non-target stills mixed with unlabeled patches extracted from the corresponding face trajectories captured with surveillance cameras. During operations, the most competent classifiers per given probe face are dynamically selected and weighted based on the internal criteria determined in the feature space of e-SVMs. This chapter also investigates the impact of using different training schemes for DA, as well as, the validation set of non-target faces extracted from stills and video trajectories of unknown individuals in the operational domain. The results indicate that the proposed system can surpass state-of-the-art accuracy, yet with a significantly lower computational complexity. In Chapter 5, a deep convolutional neural network (CNN) is proposed to cope with the discrepancies between facial regions of interest (ROIs) isolated in still and video faces for robust still-to-video FR. To that end, a face-flow autoencoder CNN called FFA-CNN is trained using both still and video ROIs in a supervised end-to-end multi-task learning. A novel loss function containing a weighted combination of pixel-wise, symmetry-wise and identity preserving losses is introduced to optimize the network parameters. The proposed FFA-CNN incorporates a reconstruction network and a fully-connected classification network, where the former reconstructs a well-illuminated frontal ROI with neutral expression from a pair of low-quality non-frontal video ROIs and the latter is utilized to compare the still and video representations to provide matching scores. Thus, integrating the proposed weighted loss function with a supervised end-to-end training approach leads to generate high-quality frontal faces and learn discriminative face representations similar for the same identities. Simulation results obtained over challenging COX Face DB confirm the effectiveness of the proposed FFA-CNN to achieve convincing performance compared to current state-of-the-art CNN-based FR systems

Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions

This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation

RECOGNITION OF FACES FROM SINGLE AND MULTI-VIEW VIDEOS

Face recognition has been an active research field for decades. In recent years, with videos playing an increasingly important role in our everyday life, video-based face recognition has begun to attract considerable research interest. This leads to a wide range of potential application areas, including TV/movies search and parsing, video surveillance, access control etc. Preliminary research results in this field have suggested that by exploiting the abundant spatial-temporal information contained in videos, we can greatly improve the accuracy and robustness of a visual recognition system. On the other hand, as this research area is still in its infancy, developing an end-to-end face processing pipeline that can robustly detect, track and recognize faces remains a challenging task. The goal of this dissertation is to study some of the related problems under different settings. We address the video-based face association problem, in which one attempts to extract face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases. We next propose a novel video-based face recognition framework. We address the problem from two different aspects: To handle pose variations, we learn a Structural-SVM based detector which can simultaneously localize the face fiducial points and estimate the face pose. By adopting a different optimization criterion from existing algorithms, we are able to improve localization accuracy. To model other face variations, we use intra-personal/extra-personal dictionaries. The intra-personal/extra-personal modeling of human faces has been shown to work successfully in the Bayesian face recognition framework. It has additional advantages in scalability and generalization, which are of critical importance to real-world applications. Combining intra-personal/extra-personal models with dictionary learning enables us to achieve state-of-arts performance on unconstrained video data, even when the training data come from a different database. Finally, we present an approach for video-based face recognition using camera networks. The focus is on handling pose variations by applying the strength of the multi-view camera network. However, rather than taking the typical approach of modeling these variations, which eventually requires explicit knowledge about pose parameters, we rely on a pose-robust feature that eliminates the needs for pose estimation. The pose-robust feature is developed using the Spherical Harmonic (SH) representation theory. It is extracted using the surface texture map of a spherical model which approximates the subject's head. Feature vectors extracted from a video are modeled as an ensemble of instances of a probability distribution in the Reduced Kernel Hilbert Space (RKHS). The ensemble similarity measure in RKHS improves both robustness and accuracy of the recognition system. The proposed approach outperforms traditional algorithms on a multi-view video database collected using a camera network