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Summary and Conclusions


In this paper we have presented an extensive survey of machine recognition of human faces and a brief review of related psychological studies. We have considered two types of face recognition tasks: one from still images and the other from video. We have categorized the methods used for each type, and discussed their characteristics and their pros and cons. In addition to a detailed review of representative work, we have provided summaries of current developments and of challenging issues. We have also identified two important issues in practical face recognition systems: the illumination problem and the pose problem. We have categorized proposed methods of solving these problems and discussed the pros and cons of these methods. To emphasize the importance of system evaluation, three sets of evaluations were described: FERET, FRVT, and XM2VTS.

Getting started in performing experiments in face recognition is very easy. The Colorado State University's Evaluation of Face Recognition Algorithms website, has an archive of baseline face recognition algorithms. Baseline algorithms available are PCA, LDA, elastic bunch graph matching, and Bayesian Intrapersonal / Extrapersoanl Image Diffference Classifier. Source code and scripts for running the algorithms can be downloaded. The website includes scripts for running the FERET Sep96 evaluation protocol (the FERET data set needs to obtained from the FERET website). The baseline algorithms and FERET Sep96 protocol provide a framework for benchmarking new algorithms. The scripts can be modified to run different sets of images against the baseline. For on-line resources related to face recognition, such as research papers and databases, see Table gif.

We give below a concise summary, followed by conclusions, in the same order as the topics appear in the paper.

To conclude our paper, we present a conjecture about face recognition based on psychological studies and lessons learned from designing algorithms. We conjecture that different mechanisms are involved in human recognition of familiar and unfamiliar faces. For example, it is possible that 3D head models are constructed, by extensive training for familiar faces, but for unfamiliar faces, multi-view 2D images are stored. This implies that we have full probability density functions for familiar faces; while for unfamiliar faces we only have discriminant functions.

Table: Internet resources for research and databases.


next up previous contents
Next: References Up: No Title Previous: Introduction

Wenyi Zhao
Mon Mar 8 16:07:04 EST 2004