A survey of face recognition algorithms and testing results

Automated face recognition (AFR) has received increased attention. We describe two general approaches to the problem and discuss their effectiveness and robustness with respect to several possible applications. We also discuss some issues of run-time performance. The AFR technology falls into three main subgroups, which represent more-or-less independent approaches to the problem: neural network solutions, eigenface solutions, and wavelet/elastic matching solutions. Each of these first requires that a facial image be identified in a scene, a process called segmentation. The image should be normalized to some extent. Normalization is usually a combination of linear translation, rotation and scaling, although the elastic matching method includes spatial transformations.