Feature Extraction of Face Image Based on Convolutional Neural Network

In order to overcome the problem of inaccuracy in feature extraction when the corner features are not obvious in traditional corner detection methods, a face feature extraction method based on deep learning convolutional neural network is proposed in this paper. The key point of this method is the construction algorithm of convolution network with single image as input data. In this paper, the convolutional neural network implemented by Python code includes three convolution layers, three lower sampling layers and one full connection layer. The filter structure is designed and the characteristic image is obtained. The convolution layer uses the Reul function as the activation function, while the lower sampling layer uses the maximum pooling and ruul activation functions. The experimental results show that the method based on the different levels of image features extracted by deep learning network as the basis for image matching, and can still achieve good recognition effect when the traditional features such as corners are not clear.