TY - JOUR
T1 - Improving the eigenphase method for face recognition
AU - Olivares-Mercado, Jesus
AU - Hotta, Kazuhiro
AU - Takahashi, Haruhisa
AU - Nakano-Miyatake, Mariko
AU - Toscano-Medina, Karina
AU - Perez-Meana, Hector
PY - 2009/8/10
Y1 - 2009/8/10
N2 - This paper proposes an improvement to the Eigenphases method, in which the image is normalized to reduce the illumination and facial expression effects and the Principal Components Analysis (PCA) is used for feature extraction, while the Gaussian Mixture Model (GMM) is used to improve the performance of classification stage. An important advantage of GMM is that this system is trained without supervisor and constructs an independent model for each user. The proposed method is evaluated using the "AR Face Database, which includes the face images of 120 subjects (65 males and 55 females). Evaluation results show that the proposed method provides better per-formance than the original eigenphases method.
AB - This paper proposes an improvement to the Eigenphases method, in which the image is normalized to reduce the illumination and facial expression effects and the Principal Components Analysis (PCA) is used for feature extraction, while the Gaussian Mixture Model (GMM) is used to improve the performance of classification stage. An important advantage of GMM is that this system is trained without supervisor and constructs an independent model for each user. The proposed method is evaluated using the "AR Face Database, which includes the face images of 120 subjects (65 males and 55 females). Evaluation results show that the proposed method provides better per-formance than the original eigenphases method.
KW - Eigenphases
KW - Face recognition and verification
KW - GMM
KW - Phase spectrum
UR - http://www.scopus.com/inward/record.url?scp=68649124028&partnerID=8YFLogxK
U2 - 10.1587/elex.6.1112
DO - 10.1587/elex.6.1112
M3 - Artículo
SN - 1349-2543
VL - 6
SP - 1112
EP - 1117
JO - IEICE Electronics Express
JF - IEICE Electronics Express
IS - 15
ER -