Face Key Point Location Method based on Parallel Convolutional Neural Network

Abstract

Based on convolutional neural network and face detection algorithm, this paper proposes a training sample expansion strategy, and a parallel convolutional network face detection algorithm for face features, occlusion and illumination detection, combined with Relu activation function and Dropout random regularization strategy. Network training not only speeds up the convergence of the network, but also improves the generalization ability. On this basis, the software based on face detection and feature point location is designed to realize the automatic loading of images and the face recognition function, to achieve accurate positioning of the face points, and to locate experiments on the LWF face database. The results show that the method is greatly improved in accuracy and reliability, and it can achieve robust and accurate estimation of key points.