基于级联滤波器深度学习的铁路安检人脸识别与验证研究

使用传统的全联通卷积神经网络（CNN）进行人脸识别和验证存在测试时间长、识别率低的问题。通过在铁路安检中使用多层（非线性）级联滤波器进行全向的抗噪声人脸识别与验证,提出一种基于级联滤波器深度学习的人脸识别和验证方法。与使用优化的全联通CNN相比,级联滤波器应用非线性阈值函数能有效提高滤波识别准确率和缩短识别时间。实验结果表明,这种结构可以级联以形成多层级联滤波器,平均识别率优于全联通CNN 8%以上,并在识别效率上提高3倍以上。最后,给出两层级联滤波器在人脸识别和验证中的性能,为铁路安检中的身份验证提供了理论支持。

Cascaded filter deep learning based face recognition and verification in railway security checking

Using traditional fully connected Convolutional Neural Network (CNN) for face recognition and verification task has the problems of long training time and low recognition rate. Through using cascaded layer (nonlinear) filters to take omnidirectional anti-noise face recognition and verification in railway security checking, this paper proposed a method of face recognition and verification with deep learning cascaded filters. Compared with the optimal fully connected CNN, the application of nonlinear threshold function in cascade filter can effectively improve the filtering recognition accuracy and shorten the recognition time. The experimental results show that this filter structure can be cascaded to form a multi-layer filtering system, and the performance can be improved about 8% in recognition rate and three times better in efficiency. Finally, the paper tested the performance of two-layer cascaded filters for the application of face recognition and verification, which provided theoretical support for identification verification in railway security checking.