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Facial feature localisation and subtle expression recognition based on deep convolution neural network

Facial feature localisation and subtle expression recognition based on deep convolution neural... In order to solve the problems existing in traditional face recognition methods, such as low accuracy of face feature location, poor accuracy of subtle expression recognition and long recognition time, a face feature localisation and subtle expression recognition based on deep convolution neural network is proposed. The principle of deep convolution neural network is analysed, and the feature extraction of human face is placed in convolution layer and pooling layer. The foreground and background entropy of face image are obtained by binarisation method of face image. Optical flow characteristics of all positions of frame image are obtained by using deep convolution neural network, and the recognition of facial subtle expression is completed. The experimental results show that the accuracy of the proposed method is up to 98%, the recognition accuracy of facial expression is high, and the recognition time is short. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Facial feature localisation and subtle expression recognition based on deep convolution neural network

International Journal of Biometrics , Volume 14 (3-4): 17 – Jan 1, 2022

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/ijbm.2022.124671
Publisher site
See Article on Publisher Site

Abstract

In order to solve the problems existing in traditional face recognition methods, such as low accuracy of face feature location, poor accuracy of subtle expression recognition and long recognition time, a face feature localisation and subtle expression recognition based on deep convolution neural network is proposed. The principle of deep convolution neural network is analysed, and the feature extraction of human face is placed in convolution layer and pooling layer. The foreground and background entropy of face image are obtained by binarisation method of face image. Optical flow characteristics of all positions of frame image are obtained by using deep convolution neural network, and the recognition of facial subtle expression is completed. The experimental results show that the accuracy of the proposed method is up to 98%, the recognition accuracy of facial expression is high, and the recognition time is short.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2022

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