Multi pose facial expression recognition based on convolutional neural network Online publication date: Fri, 05-Aug-2022
by Yongliang Feng
International Journal of Biometrics (IJBM), Vol. 14, No. 3/4, 2022
Abstract: In order to overcome the problems of low expression similarity and low recognition rate in multi pose facial expression recognition, a new multi pose facial expression recognition method based on convolutional neural network is proposed. The convolution layer is constructed directly by Gabor wavelet with fixed weights, and the full connection layer is constructed by support vector machine (SVM). The structure of convolution neural network is determined by matching growth rules, and the network parameters are trained by back-propagation algorithm. Adaboos algorithm is used to cut facial expression, gradient integral projection and dual threshold binarisation are used to locate eyes. The scale normalisation and grey scale normalisation are used to realise multi pose facial expression recognition. The experimental results show that the highest expression similarity is 98.43%, the recognition rate is close to 100% under different rotation angles, and the recognition rate is as high as 99.96% for different expressions.
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