TY - GEN
T1 - Rapid face recognition using hashing
AU - Shi, Qinfeng
AU - Li, Hanxi
AU - Shen, Chunhua
PY - 2010
Y1 - 2010
N2 - We propose a face recognition approach based on hashing. The approach yields comparable recognition rates with the random ℓ1 approach [18], which is considered the state-of-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 state-of-the-art.
AB - We propose a face recognition approach based on hashing. The approach yields comparable recognition rates with the random ℓ1 approach [18], which is considered the state-of-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 state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=77956008921&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2010.5540001
DO - 10.1109/CVPR.2010.5540001
M3 - Conference contribution
SN - 9781424469840
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 2753
EP - 2760
BT - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
T2 - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Y2 - 13 June 2010 through 18 June 2010
ER -