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Abstract—Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%. Index Terms—Age progression, face recognition, face verification, probabilistic eigenspaces, similarity measure. I.

We investigate facial asymmetry as a biometric under expression variation. For the first time, we have defined two types of quantified facial asymmetry measures that are easily computable from facial images and videos. Our findings show that the asymmetry measures of automatically selected facial regions capture individual differences that are relatively stable to facial expression variations. More importantly, a synergy is achieved by combining facial asymmetry information with conventional EigenFace and FisherFace methods. We have assessed the generality of these ndings across two publicly available face databases: Using a random subset of 110 subjects from the FERET database, a 38% classification error reduction rate is obtained. Error reduction rates of 45% to 100% are achieved on 55 subjects from the Cohn-Kanade AU-coded facial expression database. These results suggest that facial asymmetry may provide complementary discriminative information to human identification methods, which has been missing in automatic human identification.

Abstract—There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be “difficult ” and six algorithms surpassed humans on “easy ” face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control—humans. Index Terms—Face and gesture recognition, performance evaluation of algorithms and systems, human information processing. 1

Individual faces vary considerably in both the quality and quantity of the information they contain for recognition and for viewpoint generalization. In the present study, we assessed the typicality, recognizability, and viewpoint generalizability of individual faces using data from both human observers and from a computational model of face recognition across viewpoint change. The two-stage computational model incorporated a viewpoint alignment operation and a recognition-by-interpolation operation. An interesting aspect of this particular model is that the effects of typicality it predicts at the alignment and recognition stages dissociate, such that face typicality is beneficial for the success of the alignment process, but is adverse for the success of the recognition process. We applied a factor analysis to the covariance data for the human- and model-derived face measures across the different viewpoints and found two axes that appeared consistently across all viewpoints. Projecti...

Human facial asymmetry has long been a critical factor for evaluations of attractiveness and expressions in psychology and anthropology, although most studies are carried out qualitatively. In this work, we investigate in depth the effect of statistical facial asymmetry measurement as a biometric under expression variations. Our findings demonstrate that the asymmetry of specific facial regions captures individual differences that are robust to variation in facial expression. More importantly, our experimental results show that facial asymmetry provides discriminating power orthogonal to conventional face identification methods. The synergy of combining facial asymmetry with conventional methods is evaluated. Our work appears to be the first to show quantitatively the power of facial asymmetry as a biometric. 1 1 Motivation Human facial asymmetry has long been a critical factor for evaluation of facial attractiveness [16], and expressions [15] in psychology and anthropology, albeit ...

Face recognition is one of the most interesting and challenging problems in computer vision. In the past, many facets of this problem have been rigorously investigated because of its importance for understanding our cognitive process and its usefulness in various applications. A great di#culty in face recognition is the separation of intrinsic facial characteristics from extrinsic image variations. Among the latter in 2D images is pose, illumination, and expression. Unfortunately, most past studies have provided variation-speci#c solutions that are not applicable to other types of variation. Performance has remained inferior to human ability and sub-optimal for practical use. This dissertation proposes a novel solution to one of these problems. We focus on processing head pose information in 2D images: analyzing, synthesizing, and identifying facial images with arbitrary pose. Successful handling of head pose variation is one of the key factors for realizing facial information processing systems in virtually any realistic and practical scenario. Our goal is twofold. One is to provide a simple and general framework whichmay be useful beyond the speci#c problem of head pose. The other is to improve the pose processing accuracy of previous studies by using this framework. xv We propose a localized two-stage linear system which is learned strictly from sample statistics and models shape and texture information separately. Instead of using variation-speci#c analytical knowledge of 3D rotation in Euclidean space, our solution utilizes a simple statistical learning framework whose applicability is not limited to the problem at hand. A wider range of head poses is covered byanumber of local linear models distributed over various poses, each of which realizes a continuous mapp...

Abstract not found

Human facial asymmetry has long been a critical factor for evaluations of attractiveness and facial expressions in psychology and anthropology. However, it has rarely been used in human identification tasks. We investigate the effect of quantified statistical facial asymmetry as a biometric under expression variations. Our findings show that the asymmetry measure of automatically selected facial regions captures individual differences that are relatively stable to facial expression variations. More importantly, the synergy of combining Asymmetry faces with conventional Fisherface method using data from a publicly available face databases (Cohn-Kanade and FERET) is quantitatively evaluated. The improvements on classification accuracy are shown to be statistically significant. Five different experiments are setup for Fisherfaces on 55 subjects from Cohn-Kanade database: (1) train on anger and disgust test on joy, (2) train on joy and anger test on disgust, (3) train on disgust and joy test on anger, (4) train on neutral faces and test on peak expressions, and (5) train on peak expressions test on neutral faces.

Recognition of faces and objects is impaired when illumination direction varies. Three experiments explore whether this impairment can be explained by display changes (Biederman & Bar, 1999), and whether cast shadows help or hinder face recognition. Observers judged whether two sequentially-presented faces, shown with or without cast shadows, were the same person. The faces were illuminated from the same or different directions, and were presented in the same or different positions on the screen. In Experiment 1, performance was illumination-dependent only on same-position trials, suggesting that observers used display changes. Experiment 2 tested whether this could be explained by peripheral viewing on different-position trials. A fixation cross cued each face’s location, such that observers could move their eyes to view each face centrally. Performance was illumination-dependent regardless of whether position changed. In both experiments, shadows did not affect performance, in contrast to earlier findings (Braje, Kersten, Tarr, & Troje, 1998). In Experiment 3, all faces were presented peripherally without shadows. Changing the illumination direction did not affect performance. These results demonstrate that peripheral viewing, rather than display changes, can explain why changes in illumination direction do not affect performance when position changes. The results also suggest that face representations retain illumination information.