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

Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Probabilistic Sparse Subspace Clustering Using Delayed Association

Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Similar to some recent works, we integrate these two steps using a joint optimization approach. We make the following contributions: (i) we estimate the reliability of the cluster assignment for each point before assigning a point to a subspace. We group the data points into two groups of "certain" and "uncertain", with the assignment of latter group delayed until their subspace association certainty improves. (ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i.e. when subspaces intersect or data are contaminated with outliers/noise. (iii) We demonstrate experimentally that such delayed probabilistic association leads to a more accurate self-representation and final clusters. The proposed method has higher accuracy both for points that exclusively lie in one subspace, and those that are on the intersection of subspaces. (iv) We show that delayed association leads to huge reduction of computational cost, since it allows for incremental spectral clustering

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

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

3D Non-Rigid Reconstruction with Prior Shape Constraints

3D non-rigid shape recovery from a single uncalibrated camera is a challenging, under-constrained problem in computer vision. Although tremendous progress has been achieved towards solving the problem, two main limitations still exist in most previous solutions. First, current methods focus on non-incremental solutions, that is, the algorithms require collection of all the measurement data before the reconstruction takes place. This methodology is inherently unsuitable for applications requiring real-time solutions. At the same time, most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. Note that specific types of shape variation might be governed by only a small number of parameters and therefore can be well-represented in a low dimensional manifold. The methods proposed in this thesis aim to estimate the non-rigid shapes and the corresponding camera trajectories, based on both the observations and the prior learned manifold. Firstly, an incremental approach is proposed for estimating the deformable objects. An important advantage of this method is the ability to reconstruct the 3D shape from a newly observed image and update the parameters in 3D shape space. However, this recursive method assumes the deformable shapes only have small variations from a mean shape, thus is still not feasible for objects subject to large scale deformations. To address this problem, a series of approaches are proposed, all based on non-linear manifold learning techniques. Such manifold is used as a shape prior, with the reconstructed shapes constrained to lie within the manifold. Those non-linear manifold based approaches significantly improve the quality of reconstructed results and are well-adapted to different types of shapes undergoing significant and complex deformations. Throughout the thesis, methods are validated quantitatively on 2D points sequences projected from the 3D motion capture data for a ground truth comparison, and are qualitatively demonstrated on real example of 2D video sequences. Comparisons are made for the proposed methods against several state-of-the-art techniques, with results shown for a variety of challenging deformable objects. Extensive experiments also demonstrate the robustness of the proposed algorithms with respect to measurement noise and missing data