TY - GEN
T1 - Facial Expression Recognition System Based on Deep Residual Fusion Neural Network
AU - Wang, Haonan
AU - Ding, Junhang
AU - Wang, Fan
AU - Ma, Zhe
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - Rich and varied facial expressions are the intuitive carriers for transmitting emotional information to each other. Due to the variety of facial expressions, the extraction of features is quite difficult. The traditional manual extraction method can neither achieve better recognition accuracy nor guarantee the recognition efficiency. This paper uses 18-layer residual neural network, and realizes permanent mapping by means of the short-circuit connection of residual modules to ensure the network capability of deep structures. At the same time, the CLBP texture features are extracted, and the two are innovatively combined to form a more representative description feature. The experimental results show that compared with the DCNN, DBN and other networks, the convergence time is shorter and the average recognition rate is 93.24%, which is nearly 5% higher.
AB - Rich and varied facial expressions are the intuitive carriers for transmitting emotional information to each other. Due to the variety of facial expressions, the extraction of features is quite difficult. The traditional manual extraction method can neither achieve better recognition accuracy nor guarantee the recognition efficiency. This paper uses 18-layer residual neural network, and realizes permanent mapping by means of the short-circuit connection of residual modules to ensure the network capability of deep structures. At the same time, the CLBP texture features are extracted, and the two are innovatively combined to form a more representative description feature. The experimental results show that compared with the DCNN, DBN and other networks, the convergence time is shorter and the average recognition rate is 93.24%, which is nearly 5% higher.
KW - Deep residual
KW - Facial expression recognition
KW - Fusion neural network
UR - https://www.scopus.com/pages/publications/85071414486
U2 - 10.1007/978-981-32-9050-1_16
DO - 10.1007/978-981-32-9050-1_16
M3 - 会议稿件
AN - SCOPUS:85071414486
SN - 9789813290495
T3 - Lecture Notes in Electrical Engineering
SP - 138
EP - 144
BT - Proceedings of 2019 Chinese Intelligent Automation Conference
A2 - Deng, Zhidong
PB - Springer Verlag
T2 - Chinese Intelligent Automation Conference, CIAC 2019
Y2 - 20 September 2019 through 22 September 2019
ER -