TY - JOUR
T1 - A sub-block-based eigenphases algorithm with optimum sub-block size
AU - Benitez-Garcia, Gibran
AU - Olivares-Mercado, Jesus
AU - Sanchez-Perez, Gabriel
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Hector
N1 - Funding Information:
We thank the National Science and Technology Council of Mexico (CONACyT) and the National Polytechnic Institute of Mexico for the financial support during the realization of this research.
PY - 2013/1
Y1 - 2013/1
N2 - Several algorithms have been proposed for constrained face recognition applications. Among them the eigenphases algorithm and some variations of it using sub-block processing, appears to be desirable alternatives because they achieves high face recognition rate, under controlled conditions. However, their performance degrades when the face images under analysis present variations in the illumination conditions as well as partial occlusions. To overcome these problems, this paper derives the optimal sub-block size that allows improving the performance of previously proposed eigenphases algorithms. Theoretical and computer evaluation results show that, using the optimal block size, the identification performance of the eigenphases algorithm significantly improves, in comparison with the conventional one, when the face image presents different illumination conditions and partial occlusions respectively. The optimal sub-block size also allows achieving a very low false acceptance and false rejection rates, simultaneously, when performing identity verification tasks, which is not possible to obtain using the conventional approach; as well as to improve the performance of other sub-block-based eigenphases methods when rank tests are performed.
AB - Several algorithms have been proposed for constrained face recognition applications. Among them the eigenphases algorithm and some variations of it using sub-block processing, appears to be desirable alternatives because they achieves high face recognition rate, under controlled conditions. However, their performance degrades when the face images under analysis present variations in the illumination conditions as well as partial occlusions. To overcome these problems, this paper derives the optimal sub-block size that allows improving the performance of previously proposed eigenphases algorithms. Theoretical and computer evaluation results show that, using the optimal block size, the identification performance of the eigenphases algorithm significantly improves, in comparison with the conventional one, when the face image presents different illumination conditions and partial occlusions respectively. The optimal sub-block size also allows achieving a very low false acceptance and false rejection rates, simultaneously, when performing identity verification tasks, which is not possible to obtain using the conventional approach; as well as to improve the performance of other sub-block-based eigenphases methods when rank tests are performed.
KW - Eigenphases
KW - Face recognition
KW - PCA
KW - Partial occlusion
KW - SVM
KW - Sub-block processing
UR - http://www.scopus.com/inward/record.url?scp=84870060553&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2012.08.023
DO - 10.1016/j.knosys.2012.08.023
M3 - Artículo
SN - 0950-7051
VL - 37
SP - 415
EP - 426
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -