Single-sample-per-person-based face recognition using fast Discriminative Multi-manifold Analysis

Abstract

This paper presents a single sample per person (SSPP)-based face recognition method. Based on the Discriminative Multi-manifold Analysis (DMMA), we propose an accelerative face recognition method which consists of three modules. First, for one person one training image sample, we use a modified of K-means method to cluster two groups of people. Second, we divide the face images into non-overlapping local patches and apply DMMA. Third, we repeat the previous two steps to obtain the binary tree projection matrix of fast DMMA. In the experiments, we test the AR database and FERET database to verify the effectiveness of SSPP-based fast DMMA face recognition process in both accuracy and speed.