关键词: 小滤波器, 深卷积神经网络, 特征点检测, 网络深度

Abstract: To solve the problem in a complex environment, such as different positions, light conditions, and barrier factors which lead to a big drop in precision by using traditional facial feature point detection algorithms, the research on the basis of theoretical knowledge is made, and the deep convolution neural network based on small filter is put forward. The algorithm introduces the small filter thought and depth-first network into deep convolution neural network model, redesigns the training in view of the facial feature points detection, and improves the effectiveness and applicability of the algorithm. By applying the algorithm in ALFW and AFW face datasets to predict five points of facial feature, and compared with several other classical algorithms analysis results, it shows that the deep convolution neural network based on the small filter in the prediction of facial feature points at five issues has better accuracy and robustness.

Key words: small filter, deep convolution neural network, feature point detection, depth of network