Beyond eigenfaces: Probabilistic matching for face recognition

被引：102

作者：

Moghaddam, B ^{[1
]}

Wahid, W ^{[1
]}

Pentland, A ^{[1
]}

机构：

[1] Mitsubishi Elect Res Lab, Cambridge, MA 02139 USA

来源：

AUTOMATIC FACE AND GESTURE RECOGNITION - THIRD IEEE INTERNATIONAL CONFERENCE PROCEEDINGS | 1998年

关键词：

D O I：

10.1109/AFGR.1998.670921

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image differences. Specifically ute model two mutually exclusive classes of variation between facial images: intra-personal (variations in appearance of the same individual, due to different expressions or lighting) and extra-personal (variations in appearance due to a difference in identity). The likelihoods for each respective class are learned from training data using eigenspace density estimation and used to compute similarity based on the a posteriori probability of membership in the intrapersonal class, and ultimately used to rank matches in the database. The performance advantage of this probabilistic technique over nearest-neighbor eigenface matching is demonstrated using results from ARPA's 1996 "FERET" face recognition competition! in which this algorithm was found to be the top performer.

引用

页码：30 / 35

页数：2