TY - GEN
T1 - Facial emotion recognition via discrete wavelet transform, principal component analysis, and cat swarm optimization
AU - Wang, Shui Hua
AU - Yang, Wankou
AU - Dong, Zhengchao
AU - Phillips, Preetha
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Facial emotion recognition is important in many academic and industrial applications. In this paper, our team proposed a novel facial emotion recognition method. First, we used discrete wavelet transform to extract wavelet coefficients from facial images. Second, principal component analysis was utilized to reduce the features. Third, a single-hidden-layer neural network was used as the classifier. Finally and most importantly, we introduced the cat swarm optimization to train the weights and biases of the classifier. The ten-fold stratified cross validation showed cat swarm optimization method achieved an overall accuracy of 89.49 ± 0.76%. It was better than genetic algorithm, particle swarm optimization, and time-varying-acceleration-coefficient particle swarm optimization. Besides, our facial emotion recognition system was better than two state-of-the-art approaches.
AB - Facial emotion recognition is important in many academic and industrial applications. In this paper, our team proposed a novel facial emotion recognition method. First, we used discrete wavelet transform to extract wavelet coefficients from facial images. Second, principal component analysis was utilized to reduce the features. Third, a single-hidden-layer neural network was used as the classifier. Finally and most importantly, we introduced the cat swarm optimization to train the weights and biases of the classifier. The ten-fold stratified cross validation showed cat swarm optimization method achieved an overall accuracy of 89.49 ± 0.76%. It was better than genetic algorithm, particle swarm optimization, and time-varying-acceleration-coefficient particle swarm optimization. Besides, our facial emotion recognition system was better than two state-of-the-art approaches.
KW - Cat swarm optimization
KW - Discrete wavelet transform
KW - Facial emotion recognition
KW - Genetic algorithm
KW - Particle swarm optimization
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85030032614&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67777-4_18
DO - 10.1007/978-3-319-67777-4_18
M3 - Conference Proceeding
AN - SCOPUS:85030032614
SN - 9783319677767
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 203
EP - 214
BT - Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings
A2 - Sun, Yi
A2 - Lu, Huchuan
A2 - Zhang, Lihe
A2 - Yang, Jian
A2 - Huang, Hua
PB - Springer Verlag
T2 - 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017
Y2 - 22 September 2017 through 23 September 2017
ER -