Rapid face recognition using hashing

We propose a face recognition approach based on hashing. The approach yields comparable recognition rates with the random ℓ₁ approach, which is considered the state-of-the-art. But our method is much faster: it is up to 150 times faster than 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 state-of-the-art.