关键词: 生物特征识别, 曲率滤波, 并行卷积神经网络, 二均值池化, 批量归一化

Abstract: Biometric identification systems should have fast and accurate classification capabilities. Aiming at the problems of traditional face detection methods, such as single feature extraction and long training time, gradient easy to disappear and over-fitting based on deep learning algorithm, a novel face detection algorithm BM-CNN(based on mixnetwork-convolutional neural network) is proposed. The algorithm firstly uses human face segmentation and image enhancement based on curvature filtering to preprocess human face image, and then uses the optimized convolutional neural network (CNN) to preprocess image feature extraction and decision classification. For the convolutional neural network, a new parallel convolutional network and a new pooling strategy are proposed. CNN uses double-mean pooling strategy and a batch normalization (BN) method and multiple types of nolinear units to improve the algorithm detection performance. BM-CNN detects the human face through the double-line convolutional neural network strategy. Finally, this paper conducts comparative experiments on NUAA and CASIA datasets. The experimental results show that the algorithm can classify the face images accurately and also has some improvement in terms of sample size and training time.

Key words: biometric feature recognition, curvature filter, double-line convolutional neural network (CNN), double-mean pooling, batch normalization