A principal component based BDNN for face recognition

Abstract

In this paper we propose a high performance two-stage hybrid structure for face recognition. The first stage is an eigenface based recognizer, which serves as a candidate faces selector. As our experience, the Top 1 recognition rate is only 65%, however the Top 10 hit rate can be up to 98.15%. The Top 10 candidate faces are similar to each other, thus these faces are called simial faces. Since the projections of tile similar faces are too close in the eigenspace, it's very hard to distinguish a target face from similar face set. Thus, we propose the ''Horizontal Average Gray Scale (HAGS)'' as a new type of feature for the second stage recognizer. A paired-Bayesian-decision neural network (pBDNN) is used for the second stage recognizer, which identifies the target from the similar faces. Supporting by the proposed feature, a pDBNN could make an accurate classification between any two similar faces. In order to demonstrate the proposed hybrid system, we conducted some experiments on an in house database, which contains 675 images taken from 135 people. The training data for the pBDNN were small orientation (-22.5 degrees, 0 degrees, 22.5 degrees), and the large orientation (-45 degrees and 45 degrees) images were for testing. Our experimental results show that the hybrid recognition structure improvs the recognition rate for 17% more than the eigenface system alone (65%) without any rejection, and 26% more with 31% of rejection.