TY - GEN
T1 - Rotated face recognition by manifold learning with auto-associative neural network
AU - Ito, Mizuki
AU - Ohyama, Wataru
AU - Wakabayashi, Tetsushi
AU - Kimura, Fumitaka
PY - 2015/5/7
Y1 - 2015/5/7
N2 - The performance of face recognition is easily affected by appearance variation by face rotation. The proposed method in this research recognizes who is a subject in the query image in which a face is captured from an arbitrary direction. The proposed method employs an auto-associative neural network for learning a manifold which represents principal variation of facial appearance in feature space due to face rotation. Our comparison where four conditions of selecting training samples for manifold learning were adopted implied that rotated third parson faces and its reference frontal face can be applicable for the manifold learning. The results in evaluation experiments with SCface database showed that the highest recognition accuracy at RANK10 is 77.5 %.
AB - The performance of face recognition is easily affected by appearance variation by face rotation. The proposed method in this research recognizes who is a subject in the query image in which a face is captured from an arbitrary direction. The proposed method employs an auto-associative neural network for learning a manifold which represents principal variation of facial appearance in feature space due to face rotation. Our comparison where four conditions of selecting training samples for manifold learning were adopted implied that rotated third parson faces and its reference frontal face can be applicable for the manifold learning. The results in evaluation experiments with SCface database showed that the highest recognition accuracy at RANK10 is 77.5 %.
UR - http://www.scopus.com/inward/record.url?scp=84937117166&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937117166&partnerID=8YFLogxK
U2 - 10.1109/FCV.2015.7103724
DO - 10.1109/FCV.2015.7103724
M3 - Conference contribution
AN - SCOPUS:84937117166
T3 - 2015 Frontiers of Computer Vision, FCV 2015
BT - 2015 Frontiers of Computer Vision, FCV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2015
Y2 - 28 January 2015 through 30 January 2015
ER -