Research on Face Liveness Detection Algorithm Using Convolutional Neural Network
LONG Min1,2, TONG Yueyang1+
1. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
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.
龙敏，佟越洋. 应用卷积神经网络的人脸活体检测算法研究[J]. 计算机科学与探索, 2018, 12(10): 1658-1670.DOI:10.3778/j.issn.1673-9418.1801009.
LONG Min, TONG Yueyang. Research on Face Liveness Detection Algorithm Using Convolutional Neural Network. Journal of Frontiers of Computer Science and Technology, 2018, 12(10): 1658-1670.DOI:10.3778/j.issn.1673-9418.1801009.