Affiliation:
1. School of Software and Microelectronics, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing 100871, P. R. China
Abstract
Facial expression recognition is one of the hotspots in the fields of computer vision and deep learning. It has very important applications in the domains of learning service recommendation, human–computer interaction and medical industry. Aiming at the problem that the traditional expression recognition method is not accurate, this paper proposes a method combining Gabor wavelet transform and convolutional neural network. Firstly, face positioning, cropping, histogram equalization and other preprocessing are performed on the expression image. Then we extract key frames of expression sequences. After that the Gabor wavelet transform is performed on the expression image to obtain magnitude and phase characteristics. Finally, we design a 2-channel CNN for training and classification. The experiment achieves an accuracy of 96.81% on the CK+ database and it has a certain improvement compared with the Gabor wavelet transform and the traditional CNN alone.
Funder
National Natural Science Foundation of China
CERNET Innovation Project
National Key Research and Development Program of China
Publisher
World Scientific Pub Co Pte Lt
Subject
Applied Mathematics,Information Systems,Signal Processing
Cited by
19 articles.
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