... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition,

Abstract—Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing human faces for the last 20 years. While some systems, especially template-based ones, have been quite successful on expressionless, frontal views of faces with controlled lighting, not much work has taken face recognizers beyond these narrow imaging conditions. Our goal is to build a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. To recognize a novel view, the recognizer locates the eyes and nose features, uses these locations to geometrically register the input with model views, and then uses correlation on model templates to find the best match in the data base of people. Our system has achieved a recognition rate of 98% on a data base...

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of example views at different poses. But what if we only have one example view available, such as a scanned passport photo -- can we still recognize faces under different poses? Given one example view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We will develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views in a view-based, pose-invariant face recognizer. Our experiments suggest that for expressing prior knowledge of faces, 2D example-based approaches should be considered ...

The classical way of attempting to solve the face (or object) recognition problem is by using large and representative datasets. In many applications though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded and expression variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g. eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches, where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the bases of how much of this local area is affected by the expression displayed on the current test image.

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appear# ance, 3D pose, facial expression and lighting. We describe a compact parametrised model of facial appearance which takes into account all these sources of variability. The model represents both shape and grey-level appearance and is created by performing a statistical analysis over a training set of face images. A robust multi-resolution search algo# rithm is used to fit the model to faces in new images. This allows the main facial features to be located and a set of shape and grey-level appearance parameters to be recov# ered. A good approximation to a given face can be recon# structed using less than 100 of these parameters. This repre# sentation can be used for tasks such as image coding, person identification, pose recovery, gender recognition and ex# pression recognition. The system performs well on all the tasks listed above. 1: Introduction Á ÂÄÀÅÃÇÂÉÀÊÅËÂÈ...

We describe the use of flexible models for representing the shape and grey-level appearance of human faces. These models are controlled by a small number of parameters which can be used to code the overall appearance of a face for image compression and classification purposes. The model parameters control both inter-class and within-class variation. Discriminant analysis techniques are employed to enhance the effect of those parameters affecting inter-class variation, which are useful for classification. We have performed experiments on face coding and reconstruction and automatic face identification. Good recognition rates are obtained even when significant variation in lighting, expression and 3D viewpoint, is allowed.

We propose a probabilistic similarity measure for direct image matching based on a Bayesian analysis of image deformations. We model two classes of variation in object appearance: intra-object and extra-object. The probability density functions for each class are then estimated from training data and used to compute a similarity measure based on the a posteriori probabilities. Furthermore, we use a novel representation for characterizing image differences using a deformable technique for obtaining pixel-wise correspondences. This representation, which is based on a deformable 3D mesh in XYI-space, is then experimentally compared with two simpler representations: intensity differences and optical flow. The performance advantage of our deformable matching technique is demonstrated using a typically hard test set drawn from the US Army's FERET face database.

Face images and hand gestures provide a very ef# fective means of visual communication between humans. Similarly automatic face and gesture rec# ognition systems could be employed for contactless human-machine interaction systems. How# ever, developing such system is difficult, because faces and hands are both complex and highly vari# able structures. We describe how flexible models can be used to represent appearance variations of faces and hands and how these models can be used for tracking and interpretation. Experimental re# sults are presented for face pose recovery, face identification, expression recognition and gesture interpretation. 1 Introduction This paper addresses the problem of locating and interpreting faces and hand gestures in images. By interpreting face images we mean recovering the 3D pose, identifying the individual and recognising the expression; for the hand images we mean re# cognising the configuration of the fingers. In both cases different instances of the ...

This paper deals with the problem of finding facial features in images, a problem which arises in face recognition and in a number of other applications, especially in human-computer interaction, which derive information from human faces. This paper describes a system for finding faces in images and for finding facial features given the estimated face location. The techniques, based on Fisher's linear discriminant and distance from feature space, are presented, and results are presented on faces from the FERET database. The paper further describes how feature collocation statistics can be used to verify feature locations and estimate the locations of missing features.