Local quality-based matching of faces for watchlist screening applications

Video surveillance systems are often exploited by safety organizations for enhanced security and situational awareness. A key application in video surveillance is watchlist screening where target individuals are enrolled to a still-to-video Face Recognition (FR) system using single still images captured a priori under controlled conditions. Watchlist Screening is a very challenging application. Indeed, the latter must provide accurate decisions and timely recognition using limited number of reference faces for the system’s enrolment. This issue is often called the "Single Sample Per Person" (SSPP) problem. Added to that, uncontrolled factors such as variations in illumination pose and occlusion is unpreventable in real case video surveillance which causes the degradation of the FR system’s performance. Another major problem in such applications is the camera interoperability. This means that there is a huge gap between the camera used for taking the still images and the camera used for taking the video surveillance footage in terms of quality and resolution. This issue hinders the classification process then decreases the system‘s performance. Controlled and uniform lighting is indispensable for having good facial captures that contributes in the recognition performance of the system. However, in reality, facial captures are poor in illumination factor and are severely affecting the system’s performance. This is why it is important to implement a FR system which is invariant to illumination changes. The first part of this Thesis consists in investigating different illumination normalization (IN) techniques that are applied at the pre-processing level of the still-to-video FR. Afterwards IN techniques are compared to each other in order to pinpoint the most suitable technique for illumination invariance. In addition, patch-based methods for template matching extracts facial features from different regions which offers more discriminative information and deals with occlusion issues. Thus, local matching is applied for the still-to-video FR system. For that, a profound examination is needed on the manner of applying these IN techniques. Two different approaches were conducted: the global approach which consists in performing IN on the image then performs local matching and the local approach which consists in primarily dividing the images into non overlapping patches then perform on individually on each patch each IN technique. The results obtained after executing these experiments have shown that the Tan and Triggs (TT) and Multi ScaleWeberfaces are likely to offer better illumination invariance for the still-to-video FR system. In addition to that, these outperforming IN techniques applied locally on each patch have shown to improve the performance of the FR compared to the global approach. The performance of a FR system is good when the training data and the operation data are from the same distribution. Unfortunately, in still-to-video FR systems this is not satisfied. The training data are still, high quality, high resolution and frontal images. However, the testing data are video frames, low quality, low resolution and varying head pose images. Thus, the former and the latter do not have the same distribution. To address this domain shift, the second part of this Thesis consists in presenting a new technique of dynamic regional weighting exploiting unsupervised domain adaptation and contextual information based on quality. The main contribution consists in assigning dynamic weights that is specific to a camera domain.This study replaces the static and predefined manner of assigning weights. In order to assess the impact of applying local weights dynamically, results are compared to a baseline (no weights) and static weighting technique. This context based approach has proven to increase the system’s performance compared to the static weighting that is dependent on the dataset and the baseline technique which consists of having no weights. These experiments are conducted and validated using the ChokePoint Dataset. As for the performance of the still-to-video FR system, it is evaluated using performance measures, Receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve analysis

Learning from imbalanced data in face re-identification using ensembles of classifiers

Face re-identification is a video surveillance application where systems for video-to-video face recognition are designed using faces of individuals captured from video sequences, and seek to recognize them when they appear in archived or live videos captured over a network of video cameras. Video-based face recognition applications encounter challenges due to variations in capture conditions such as pose, illumination etc. Other challenges in this application are twofold; 1) the imbalanced data distributions between the face captures of the individuals to be re-identified and those of other individuals 2) varying degree of imbalance during operations w.r.t. the design data. Learning from imbalanced data is challenging in general due in part to the bias of performance in most two-class classification systems towards correct classification of the majority (negative, or non-target) class (face images/frames captured from the individuals in not to be re-identified) better than the minority (positive, or target) class (face images/frames captured from the individual to be re-identified) because most two-class classification systems are intended to be used under balanced data condition. Several techniques have been proposed in the literature to learn from imbalanced data that either use data-level techniques to rebalance data (by under-sampling the majority class, up-sampling the minority class, or both) for training classifiers or use algorithm-level methods to guide the learning process (with or without cost sensitive approaches) such that the bias of performance towards correct classification of the majority class is neutralized. Ensemble techniques such as Bagging and Boosting algorithms have been shown to efficiently utilize these methods to address imbalance. However, there are issues faced by these techniques in the literature: (1) some informative samples may be neglected by random under-sampling and adding synthetic positive samples through upsampling adds to training complexity, (2) cost factors must be pre-known or found, (3) classification systems are often optimized and compared using performance measurements (like accuracy) that are unsuitable for imbalance problem; (4) most learning algorithms are designed and tested on a fixed imbalance level of data, which may differ from operational scenarios; The objective of this thesis is to design specialized classifier ensembles to address the issue of imbalance in the face re-identification application and as sub-goals avoiding the abovementioned issues faced in the literature. In addition achieving an efficient classifier ensemble requires a learning algorithm to design and combine component classifiers that hold suitable diversity-accuracy trade off. To reach the objective of the thesis, four major contributions are made that are presented in three chapters summarized in the following. In Chapter 3, a new application-based sampling method is proposed to group samples for under-sampling in order to improve diversity-accuracy trade-off between classifiers of the ensemble. The proposed sampling method takes the advantage of the fact that in face re-identification applications, facial regions of a same person appearing in a camera field of view may be regrouped based on their trajectories found by face tracker. A partitional Bagging ensemble method is proposed that accounts for possible variations in imbalance level of the operational data by combining classifiers that are trained on different imbalance levels. In this method, all samples are used for training classifiers and information loss is therefore avoided. In Chapter 4, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loosing information while generating a diverse pool of classifiers. From one iteration to the next, the PBoost algorithm accumulates these uncorrelated groups of samples into a set that grows gradually in size and imbalance. This algorithm is more sophisticated than the one proposed in Chapter 3 because instead of training the base classifiers on this set, the base classifiers are trained on balanced subsets sampled from this set and validated on the whole set. Therefore, the base classifiers are more accurate while the robustness to imbalance is not jeopardized. In addition, the sample selection is based on the weights that are assigned to samples which correspond to their importance. In addition, the computation complexity of PBoost is lower than Boosting ensemble techniques in the literature for learning from imbalanced data because not all of the base classifiers are validated on all negative samples. A new loss factor is also proposed to be used in PBoost to avoid biasing performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set according to the ability of classifiers to recognize both classes. In comparing the performance of the classifier systems in Chapter 3 and 4, a need is faced for an evaluation space that compares classifiers in terms of a suitable performance metric over all of their decision thresholds, different imbalance levels of test data, and different preference between classes. The F-measure is often used to evaluate two-class classifiers on imbalanced data, and no global evaluation space was available in the literature for this measure. Therefore, in Chapter 5, a new global evaluation space for the F-measure is proposed that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of possible imbalance levels for the desired preference of true positive rate to precision. These properties are missing in ROC and precision-recall spaces. This space also allows us to empirically improve the performance of specialized ensemble learning methods for imbalance under a given operating condition. Through a validation, the base classifiers are combined using a modified version of the iterative Boolean combination algorithm such that the selection criterion in this algorithm is replaced by F-measure instead of AUC, and the combination is carried out for each operating condition. The proposed approaches in this thesis were validated and compared using synthetic data and videos from the Faces In Action, and COX datasets that emulate face re-identification applications. Results show that the proposed techniques outperforms state of the art techniques over different levels of imbalance and overlap between classes

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

Adaptive classifier ensembles for face recognition in video-surveillance

Lors de l’implémentation de systèmes de sécurité tels que la vidéo-surveillance intelligente, l’utilisation d’images de visages présente de nombreux avantages par rapport à d’autres traits biométriques. En particulier, cela permet de détecter d’éventuels individus d’intérêt de manière discrète et non intrusive, ce qui peut être particulièrement avantageux dans des situations comme la détection d’individus sur liste noire, la recherche dans des données archivées ou la ré-identification de visages. Malgré cela, la reconnaissance de visages reste confrontée à de nombreuses difficultés propres à la vidéo surveillance. Entre autres, le manque de contrôle sur l’environnement observé implique de nombreuses variations dans les conditions d’éclairage, la résolution de l’image, le flou de mouvement, l’orientation et l’expression des visages. Pour reconnaître des individus, des modèles de visages sont habituellement générés à l’aide d’un nombre limité d’images ou de vidéos de référence collectées lors de sessions d’inscription. Cependant, ces acquisitions ne se déroulant pas nécessairement dans les mêmes conditions d’observation, les données de référence représentent pas toujours la complexité du problème réel. D’autre part, bien qu’il soit possible d’adapter les modèles de visage lorsque de nouvelles données de référence deviennent disponibles, un apprentissage incrémental basé sur des données significativement différentes expose le système à un risque de corruption de connaissances. Enfin, seule une partie de ces connaissances est effectivement pertinente pour la classification d’une image donnée. Dans cette thèse, un nouveau système est proposé pour la détection automatique d’individus d’intérêt en vidéo-surveillance. Plus particulièrement, celle-ci se concentre sur un scénario centré sur l’utilisateur, où un système de reconnaissance de visages est intégré à un outil d’aide à la décision pour alerter un opérateur lorsqu’un individu d’intérêt est détecté sur des flux vidéo. Un tel système se doit d’être capable d’ajouter ou supprimer des individus d’intérêt durant son fonctionnement, ainsi que de mettre à jour leurs modèles de visage dans le temps avec des nouvelles données de référence. Pour cela, le système proposé se base sur de la détection de changement de concepts pour guider une stratégie d’apprentissage impliquant des ensembles de classificateurs. Chaque individu inscrit dans le système est représenté par un ensemble de classificateurs à deux classes, chacun étant spécialisé dans des conditions d’observation différentes, détectées dans les données de référence. De plus, une nouvelle règle pour la fusion dynamique d’ensembles de classificateurs est proposée, utilisant des modèles de concepts pour estimer la pertinence des classificateurs vis-à-vis de chaque image à classifier. Enfin, les visages sont suivis d’une image à l’autre dans le but de les regrouper en trajectoires, et accumuler les décisions dans le temps. Au Chapitre 2, la détection de changement de concept est dans un premier temps utilisée pour limiter l’augmentation de complexité d’un système d’appariement de modèles adoptant une stratégie de mise à jour automatique de ses galeries. Une nouvelle approche sensible au contexte est proposée, dans laquelle seules les images de haute confiance capturées dans des conditions d’observation différentes sont utilisées pour mettre à jour les modèles de visage. Des expérimentations ont été conduites avec trois bases de données de visages publiques. Un système d’appariement de modèles standard a été utilisé, combiné avec un module de détection de changement dans les conditions d’illumination. Les résultats montrent que l’approche proposée permet de diminuer la complexité de ces systèmes, tout en maintenant la performance dans le temps. Au Chapitre 3, un nouveau système adaptatif basé des ensembles de classificateurs est proposé pour la reconnaissance de visages en vidéo-surveillance. Il est composé d’un ensemble de classificateurs incrémentaux pour chaque individu inscrit, et se base sur la détection de changement de concepts pour affiner les modèles de visage lorsque de nouvelles données sont disponibles. Une stratégie hybride est proposée, dans laquelle des classificateurs ne sont ajoutés aux ensembles que lorsqu’un changement abrupt est détecté dans les données de référence. Lors d’un changement graduel, les classificateurs associés sont mis à jour, ce qui permet d’affiner les connaissances propres au concept correspondant. Une implémentation particulière de ce système est proposée, utilisant des ensembles de classificateurs de type Fuzzy-ARTMAP probabilistes, générés et mis à jour à l’aide d’une stratégie basée sur une optimisation par essaims de particules dynamiques, et utilisant la distance de Hellinger entre histogrammes pour détecter des changements. Les simulations réalisées sur la base de donnée de vidéo-surveillance Faces in Action (FIA) montrent que le système proposé permet de maintenir un haut niveau de performance dans le temps, tout en limitant la corruption de connaissance. Il montre des performances de classification supérieure à un système similaire passif (sans détection de changement), ainsi qu’a des systèmes de référence de type kNN probabiliste, et TCM-kNN. Au Chapitre 4, une évolution du système présenté au Chapitre 3 est proposée, intégrant des mécanismes permettant d’adapter dynamiquement le comportement du système aux conditions d’observation changeantes en mode opérationnel. Une nouvelle règle de fusion basée sur de la pondération dynamique est proposée, assignant à chaque classificateur un poids proportionnel à son niveau de compétence estimé vis-à-vis de chaque image à classifier. De plus, ces compétences sont estimées à l’aide des modèles de concepts utilisés en apprentissage pour la détection de changement, ce qui permet un allègement des ressources nécessaires en mode opérationnel. Une évolution de l’implémentation proposée au Chapitre 3 est présentée, dans laquelle les concepts sont modélisés à l’aide de l’algorithme de partitionnement Fuzzy C-Means, et la fusion de classificateurs réalisée avec une moyenne pondérée. Les simulation expérimentales avec les bases de données de vidéo-surveillance FIA et Chokepoint montrent que la méthode de fusion proposée permet d’obtenir des résultats supérieurs à la méthode de sélection dynamique DSOLA, tout en utilisant considérablement moins de ressources de calcul. De plus, la méthode proposée montre des performances de classification supérieures aux systèmes de référence de type kNN probabiliste, TCM-kNN et Adaptive Sparse Coding

Adaptive multi-classifier systems for face re-identification applications

In video surveillance, decision support systems rely more and more on face recognition (FR) to rapidly determine if facial regions captured over a network of cameras correspond to individuals of interest. Systems for FR in video surveillance are applied in a range of scenarios, for instance in watchlist screening, face re-identification, and search and retrieval. The focus of this Thesis is video-to-video FR, as found in face re-identification applications, where facial models are designed on reference data, and update is archived on operational captures from video streams. Several challenges emerge from the task of recognizing individuals of interest from faces captured with video cameras. Most notably, it is often assumed that the facial appearance of target individuals do not change over time, and the proportions of faces captured for target and non-target individuals are balanced, known a priori and remain fixed. However, faces captured during operations vary due to several factors, including illumination, blur, resolution, pose expression, and camera interoperability. In addition, facial models used matching are commonly not representative since they are designed a priori, with a limited amount of reference samples that are collected and labeled at a high cost. Finally, the proportions of target and non-target individuals continuously change during operations. In literature, adaptive multiple classifier systems (MCSs) have been successfully applied to video-to-video FR, where the facial model for each target individual is designed using an ensemble of 2-class classifiers (trained using target vs. non-target reference samples). Recent approaches employ ensembles of 2-class Fuzzy ARTMAP classifiers, with a DPSO strategy to generate a pool of classifiers with optimized hyperparameters, and Boolean combination to merge their responses in the ROC space. Besides, the skew-sensitive ensembles were recently proposed to adapt the fusion function of an ensemble according to class imbalance measured on operational data. These active approaches estimate target vs. non-target proportions periodically during operations distance, and the fusion of classifier ensembles are adapted to such imbalance. Finally, face tracking can be used to regroup the system responses linked to a facial trajectory (facial captures from a single person in the scene) for robust spatio-temporal recognition, and to update facial models over time using operational data. In this Thesis, new techniques are proposed to adapt the facial models for individuals enrolled to a video-to-video FR system. Trajectory-based self-updating is proposed to update the system, considering gradual and abrupt changes in the classification environment. Then, skew-sensitive ensembles are proposed to adapt the system to the operational imbalance. In Chapter 2, an adaptive framework is proposed for partially-supervised learning of facial models over time based on facial trajectories. During operations, information from a face tracker and individual-specific ensembles is integrated for robust spatio-temporal recognition and for self-update of facial models. The tracker defines a facial trajectory for each individual in video. Recognition of a target individual is done if the positive predictions accumulated along a trajectory surpass a detection threshold for an ensemble. If the accumulated positive predictions surpass a higher update threshold, then all target face samples from the trajectory are combined with non-target samples (selected from the cohort and universal models) to update the corresponding facial model. A learn-and-combine strategy is employed to avoid knowledge corruption during self-update of ensembles. In addition, a memory management strategy based on Kullback-Leibler divergence is proposed to rank and select the most relevant target and non-target reference samples to be stored in memory as the ensembles evolves. The proposed system was validated with synthetic data and real videos from Face in Action dataset, emulating a passport checking scenario. Initially, enrollment trajectories were used for supervised learning of ensembles, and videos from three capture sessions were presented to the system for FR and self-update. Transaction-level analysis shows that the proposed approach outperforms baseline systems that do not adapt to new trajectories, and provides comparable performance to ideal systems that adapt to all relevant target trajectories, through supervised learning. Subject-level analysis reveals the existence of individuals for which self-updated ensembles provide a considerable benefit. Trajectory-level analysis indicates that the proposed system allows for robust spatio-temporal video-to-video FR. In Chapter 3, an extension and a particular implementation of the ensemble-based system for spatio-temporal FR is proposed, and is characterized in scenarios with gradual and abrupt changes in the classification environment. Transaction-level results show that the proposed system allows to increase AUC accuracy by about 3% in scenarios with abrupt changes, and by about 5% in scenarios with gradual changes. Subject-based analysis reveals the difficulties of FR with different poses, affecting more significantly the lamb- and goat-like individuals. Compared to reference spatio-temporal fusion approaches, the proposed accumulation scheme produces the highest discrimination. In Chapter 4, adaptive skew-sensitive ensembles are proposed to combine classifiers trained by selecting data with varying levels of imbalance and complexity, to sustain a high level the performance for video-to-video FR. During operations, the level of imbalance is periodically estimated from the input trajectories using the HDx quantification method, and pre-computed histogram representations of imbalanced data distributions. Ensemble scores are accumulated of trajectories for robust skew-sensitive spatio-temporal recognition. Results on synthetic data show that adapting the fusion function with the proposed approach can significantly improve performance. Results on real data show that the proposed method can outperform reference techniques in imbalanced video surveillance environments