Face recognition using infrared vision

Au cours de la dernière décennie, la reconnaissance de visage basée sur l’imagerie infrarouge (IR) et en particulier la thermographie IR est devenue une alternative prometteuse aux approches conventionnelles utilisant l’imagerie dans le spectre visible. En effet l’imagerie (visible et infrarouge) trouvent encore des contraintes à leur application efficace dans le monde réel. Bien qu’insensibles à toute variation d’illumination dans le spectre visible, les images IR sont caractérisées par des défis spécifiques qui leur sont propres, notamment la sensibilité aux facteurs qui affectent le rayonnement thermique du visage tels que l’état émotionnel, la température ambiante, la consommation d’alcool, etc. En outre, il est plus laborieux de corriger l’expression du visage et les changements de poses dans les images IR puisque leur contenu est moins riche aux hautes fréquences spatiales ce qui représente en fait une indication importante pour le calage de tout modèle déformable. Dans cette thèse, nous décrivons une nouvelle méthode qui répond à ces défis majeurs. Concrètement, pour remédier aux changements dans les poses et expressions du visage, nous générons une image synthétique frontale du visage qui est canonique et neutre vis-à-vis de toute expression faciale à partir d’une image du visage de pose et expression faciale arbitraires. Ceci est réalisé par l’application d’une déformation affine par morceaux précédée par un calage via un modèle d’apparence active (AAM). Ainsi, une de nos publications est la première publication qui explore l’utilisation d’un AAM sur les images IR thermiques ; nous y proposons une étape de prétraitement qui rehausse la netteté des images thermiques, ce qui rend la convergence de l’AAM rapide et plus précise. Pour surmonter le problème des images IR thermiques par rapport au motif exact du rayonnement thermique du visage, nous le décrivons celui-ci par une représentation s’appuyant sur des caractéristiques anatomiques fiables. Contrairement aux approches existantes, notre représentation n’est pas binaire ; elle met plutôt l’accent sur la fiabilité des caractéristiques extraites. Cela rend la représentation proposée beaucoup plus robuste à la fois à la pose et aux changements possibles de température. L’efficacité de l’approche proposée est démontrée sur la plus grande base de données publique des vidéos IR thermiques des visages. Sur cette base d’images, notre méthode atteint des performances de reconnaissance assez bonnes et surpasse de manière significative les méthodes décrites précédemment dans la littérature. L’approche proposée a également montré de très bonnes performances sur des sous-ensembles de cette base de données que nous avons montée nous-mêmes au sein de notre laboratoire. A notre connaissance, il s’agit de l’une des bases de données les plus importantes disponibles à l’heure actuelle tout en présentant certains défis.Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g., emotional state, ambient temperature, etc. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency details which is an important cue for fitting any deformable model. In this thesis we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first work which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances details in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieves satisfying recognition performance and significantly outperforms previously described methods. The proposed approach has also demonstrated satisfying performance on subsets of the largest video database of the world gathered in our laboratory which will be publicly available free of charge in future. The reader should note that due to the very nature of the feature extraction method in our system (i.e., anatomical based nature of it), we anticipate high robustness of our system to some challenging factors such as the temperature changes. However, we were not able to investigate this in depth due to the limits which exist in gathering realistic databases. Gathering the largest video database considering some challenging factors is one of the other contributions of this research

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Highly accurate and fully automatic 3D head pose estimation and eye gaze estimation using RGB-D sensors and 3D morphable models

The research presented in the paper was funded by grant F506-FSA of the Auto21 Networks of Centers of Excellence Program of Canada.This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB-D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB-D sensors require either an offline step for supervised learning or 3D head model construction, which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3D rather than the complete head, which, in turn, relaxes all of the constraints in the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluated the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.Publisher PDFPeer reviewe

Highly accurate and fully automatic 3D head pose estimation and eye gaze estimation using RGB-D sensors and 3D morphable models

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB-D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB-D sensors require either an offline step for supervised learning or 3D head model construction, which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3D rather than the complete head, which, in turn, relaxes all of the constraints in the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluated the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.</p

Highly accurate and fully automatic head pose estimation from a low quality consumer-level RGB-D sensor

In this paper we describe a novel algorithm for head pose estimation from low-quality RGB-D data acquired using a consumer-level device such as Microsoft Kinect. We focus our attention on the wellknown challenges in the processing of depth point-clouds which include spurious data, noise, and missing data caused by occlusion. Our algorithm performs pose estimation by fitting a 3D morphable model which explicitly includes pose parameters. Several important novelties are described. (i) We propose a method for automatic removal of the majority of spurious depth data which uses facial feature detection in the associated RGB image. By back-projecting the corresponding image loci and intersecting them with the 3D point-cloud we construct the facial features plane used to crop the point-cloud. (ii) Both high convergence speed and high fitting accuracy are achieved by formulating the fitting objective function to include both point-to-point and point-to-plane point-cloud matching terms. (iii) The effect of misleading point-cloud matches caused by noisy or missing data is reduced by using the Tukey biweight function as a robust statistic and by employing a re-weighting scheme for different terms in the fitting objective function. (iv) Lastly, the proposed algorithm is evaluated on the standard benchmark Biwi Kinect Head Pose Database on which it is shown to outperform substantially the current state-of-the-art, achieving more than a 20-fold reduction in error estimates of all three Euler angles i.e. yaw, pitch, and roll. A thorough analysis of the results is used both to gain full insight into the behaviour of the described algorithm as well as to highlight important methodological issues which future authors should consider in the evaluation of pose estimation algorithms.</p

Highly accurate and fully automatic head pose estimation from a low quality consumer-level RGB-D sensor

In this paper we describe a novel algorithm for head pose estimation from low-quality RGB-D data acquired using a consumer-level device such as Microsoft Kinect. We focus our attention on the wellknown challenges in the processing of depth point-clouds which include spurious data, noise, and missing data caused by occlusion. Our algorithm performs pose estimation by fitting a 3D morphable model which explicitly includes pose parameters. Several important novelties are described. (i) We propose a method for automatic removal of the majority of spurious depth data which uses facial feature detection in the associated RGB image. By back-projecting the corresponding image loci and intersecting them with the 3D point-cloud we construct the facial features plane used to crop the point-cloud. (ii) Both high convergence speed and high fitting accuracy are achieved by formulating the fitting objective function to include both point-to-point and point-to-plane point-cloud matching terms. (iii) The effect of misleading point-cloud matches caused by noisy or missing data is reduced by using the Tukey biweight function as a robust statistic and by employing a re-weighting scheme for different terms in the fitting objective function. (iv) Lastly, the proposed algorithm is evaluated on the standard benchmark Biwi Kinect Head Pose Database on which it is shown to outperform substantially the current state-of-the-art, achieving more than a 20-fold reduction in error estimates of all three Euler angles i.e. yaw, pitch, and roll. A thorough analysis of the results is used both to gain full insight into the behaviour of the described algorithm as well as to highlight important methodological issues which future authors should consider in the evaluation of pose estimation algorithms.</p

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views

Infrared face recognition: a literature review

Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject