Automated face recognition has moved from science fiction to reality during the last twenty years. For high quality frontal face images, recognition errors have been cut in half every two years as more sophisticated algorithms are developed. Algorithms and key ideas in the field of face recognition that have enabled such rapid performance improvement are introduced to a statistical audience. Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state-of-the-art, explores how these concepts persist as organizing principles in the field. Algorithms based directly upon classical statistical techniques include linear methods like principle component analysis and linear discriminant analysis. Nonlinear manifold methods, such as Laplacianfaces and Stiefel quotients, offer considerable performance improvements. Other noteworthy ideas include 3-dimensional morphable models, methods using local regions and/or alternative feature spaces (e.g., elastic bunch graph matching and local binary patterns) and sparse representation approaches. Opportunities for innovative statistical and collaborative research in face recognition are expanding in tandem with the growing complexity and diversity of applications.
Citation: Computational Statistics
Pub Type: Journals