TY - GEN
T1 - Research on face recognition method based on deep learning in natural environment
AU - Yan, Jiali
AU - Zhang, Longfei
AU - Wu, Yufeng
AU - Guo, Penghui
AU - Zhang, Fuquan
AU - Tang, Shuo
AU - Ding, Gangyi
AU - Zheng, Fuquan
AU - Xu, Lin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).
AB - In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).
KW - Joint Bayesian
KW - Natural environment
KW - face recognition
KW - multipatches
UR - http://www.scopus.com/inward/record.url?scp=85049228482&partnerID=8YFLogxK
U2 - 10.1109/ICAwST.2017.8256509
DO - 10.1109/ICAwST.2017.8256509
M3 - Conference contribution
AN - SCOPUS:85049228482
T3 - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
SP - 501
EP - 506
BT - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
Y2 - 8 November 2017 through 10 November 2017
ER -