TY - GEN
T1 - Face recognition with enhanced local gabor binary pattern from human fixations
AU - Choi, Eunsoo
AU - Lee, Seong Whan
AU - Wallraven, Christian
PY - 2012
Y1 - 2012
N2 - Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. Face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.
AB - Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. Face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.
KW - Face recognition
KW - LGBP
KW - eye-tracking
KW - human perception
KW - lighting changes
UR - http://www.scopus.com/inward/record.url?scp=84872423625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872423625&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2012.6377836
DO - 10.1109/ICSMC.2012.6377836
M3 - Conference contribution
AN - SCOPUS:84872423625
SN - 9781467317146
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 863
EP - 867
BT - Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Y2 - 14 October 2012 through 17 October 2012
ER -