Abstract: The traditional convolution neural network is widely used in face recognition,but there is still a slow convergence rate.It needs to processbatch of normalization,and prevent gradient diffusion.And then the self-normalizing convolution neural network than the ordinary convolution neural network convergence speed is faster,and no need toprocess batch of normalization.Therefore,it proposes that using the self-normalizing convolution neural network to process face recognition.Firstly,the algorithmstructures a self-normalized convolution neural network consisting of two convolution layers,a pooling layer,two fully connected layers and a Softmax regression layer to process extraction and classification for facial features;then find the best experimental conditions by means of self-normalized convolution neural networks with different batch sizes and different network layers experimental comparison;finally,compared with the traditional CNN algorithm and other algorithms.The recognition rate made by the proposedmethod can reach 98.3% on ORL face database.The experimental results show that theself-normalizing convolution neural network is more suitable for face recognition than ordinary convolution neural networks,and has higher recognition rate and faster convergence speed.