Rapid Face Recognition Using Hashing

We propose a face recognition approach based on hashing. The approach yields comparable recognition rates with the random 1 approach [18], which is considered the stateof-the-art. But our method is much faster: it is up to 150 times faster than [18] on the YaleB dataset. We show that with hashing, the sparse representation can be recovered with a high probability because hashing preserves the restrictive isometry property. Moreover, we present a theoretical analysis on the recognition rate of the proposed hashing approach. Experiments show a very competitive recognition rate and significant speedup compared with the stateof-the-art.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Qinfeng Shi, Hanxi Li, Chunhua Shen
• Keywords: Computer Vision, CVPR 2010, Face Recognition Approach, Recognition Rate, Yields Comparable Recognition