In this paper we describe a holistic face recognition method based on subspace Linear Discriminant Analysis (LDA). The method consists of two steps: first we project the face image from the original vector space to a face subspace via Principal Component Analysis where the subspace dimension is carefully chosen, and then use LDA to obtain a linear classifier in the subspace. The criterion we use to choose the subspace dimension enables us to generate class-separable features via LDA. In addition, we employ a weighted distance metric guided by the LDA eigenvalues to improve the performance of the subspace LDA method. Finally, the improved performance of the subspace LDA approach is demonstrated through experiments using the FERET dataset for face recognition/verification, a large mugshot dataset for person verification, and the MPEG-7 dataset. 1 Partially supported by the Office of Naval Research under Grant N00014-95-1-0521. I. Introduction The problem of automatic face recognition...

Elastic graph matching has been proposed as a practical implementation of dynamic link matching, which is a neural network with dynamically evolving links between a reference model and an input image. Each node of the graph contains features that characterize the neighborhood of its location in the image. The elastic graph matching usually consists of two consecutive steps, namely a matching with a rigid grid, followed by a deformation of the grid, which is actually the elastic part. The deformation step is introduced in order to allow for some deformation, rotation, and scaling of the object to be matched. This method is applied here to the authentication of human faces where candidates claim an identity that is to be checked. The matching error as originally suggested is not powerful enough to provide satisfying results in this case. We introduce an automatic weighting of the nodes according to their significance. We also explore the significance of the elastic deformation for an application of face-based person authentication. We compare performance results obtained with and without the second matching step. Results show that the deformation step slightly increases the performance, but has lower influence than the weighting of the nodes. The best results are obtained with the combination of both aspects. The results provided by the proposed method compare favorably with two methods that require a prior geometric face normalization, namely the synergetic and eigenface approaches.

this article, we outline the state-of-the-art of several popular biometric modalities and technologies and provide specific applications where biometric recognition may be beneficially incorporated. In addition, we discuss integration strategies of biometric authentication technologies into DRM systems so that the whole process meets the needs and requirements of consumers, content providers, and payment brokers, securing delivery channels and contents

This paper summarizes the Bochum/USC face recognition system, our preparations for the FERET Phase III test, and test results as far as they have been made known to us. Our technology is based on Gabor wavelets and elastic bunch graph matching. We briefly discuss our technology in relation to biological and PCA based systems and indicate current activities in the lab and potential future applications.

The system presented is part of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a composition of stored graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the bunch graph, such as sex or the presence of glasses or a beard. Keywords: face analysis, sex discrimination, facial attributes, phantom faces, Gabor wavelets, elastic graph matching, bunch graph. 1 Introduction The system presented here has not primarily been designed for face processing or even sex identification. It is rather part of a larger effort to develop a general recognition system, which can be applied to faces [1] as well as to any other class of objects [2]. It has also been shown that it can deal with many differe...

Contents 1 Introduction 9 2 The a priori Constraints 13 2.1 The Bias/Variance Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 The Argument between Rationalism and Empiricism and its Resolution in Kant's Metaphysic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 The Development of the Visual System . . . . . . . . . . . . . . . . . . . . 18 2.3.1 Developmental Psychology of Visual and Gripping Abilities . . . . 19 2.3.2 Development of the Visual System: Neurobiology . . . . . . . . . . 20 2.4 The a priori Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.1 Locality and the Correspondence Problem . . . . . . . . . . . . . . 24 2.4.2 Feature Selection and Feature Organization . . . . . . . . . . . . . 25 2.4.3 Evaluation of Features . . . . . . . . . . . . . . . . . . . . . . . . . 28 3 An Algorithm for the Learning of Weights 29 3.1 Weighted Average as Discrimination Function . . . . . . . . . . . . . . . . 29 3.2 Problem

We present here a system for invariant and robust recognition of objects from camera images. The system aspires both to be a model for biological object vision (at least an ontogenetically early form of it) and to be at the cutting edge of technological achievement. Our model is based on the principles of temporal feature binding and dynamic link matching. Objects are stored in the form of two-dimensional aspects. These are competitively matched against current images. During the matching process, complete matrices of dynamic links between the image and all models are refined by a process of rapid selforganization, the final state connecting only corresponding points in image and object models. As data format for representing images we use local sets ("jets") of Gabor-based wavelets. We have tested the performance of our system by having it recognize human faces against data bases of more than one hundred images. The system is invariant with respect to retinal position, and it is robus...

We present a Bayesian recognition framework in which a model of the whole face is enhanced by models of facial feature position and appearances. Face recognition and facial expression recognition are carried out using maximum likelihood decisions. The algorithm finds the model and facial expression that maximizes the likelihood of a test image. In this framework, facial appearance matching is improved by facial expression matching. Also, changes in facial features due to expressions are used together with facial deformation patterns to jointly perform expression recognition. In our current implementation, the face is divided into 9 facial features grouped in 4 regions which are detected and tracked automatically in video segments. The feature images are modeled using Gaussian distributions on a principal component sub-space. The training procedure is supervised: we use video segments of people in which the facial expressions have been segmented and labeled by hand. We report results on ...

A framework for embedded recognition of faces and facial expressions is described. Faces are modeled based on the appearances and positions of facial features. Hidden states are used to represent discrete facial expressions. A face model is constructed for each person in the database using video segments showing different facial expressions. Face recognition and facial expression recognition are carried out using Bayesian classification. In our current implementation, the face is divided into 9 facial features grouped in 4 regions which are detected and tracked automatically in video segments. We report results on face and facial expression recognition using a video database of 18 people and 6 expressions.

An online facial expression recognition system based on personalized galleries is presented. This system is built on the framework of the PersonSpotter system, which is able to track and detect the face of a person in a live video sequence. By utilizing the recognition method of Elastic Graph Matching, the most similar person whose images are stored in the gallery can be found, then the personalized gallery of this person is used to recognize the expression on the probe face. A personalized gallery consists of images of the same person showing different facial expressions. Node weighting and weighted voting in addition to Elastic Graph Matching are applied to identify the expression. The performance achieved by this system shows its great potential. 1. Introduction Facial expression recognition just like face recognition is an important aspect in interpersonal communication and human-machine interaction. Both of them would have significant applications for building more intelligent a...