Face anti-spoofing is a relatively important part of the face recognition system, which has great significance for financial payment and access control systems. Aiming at the problems of unstable face alignment, complex lighting, and complex structure of face anti-spoofing detection network, a novel method is presented using a combination of convolutional neural network and brightness equalization. Firstly, multi-task convolutional neural network (MTCNN) based on the cascade of three convolutional neural networks (CNNs), P-net, R-net, and O-net are used to achieve accurate positioning of the face, and the detected face bounding box is cropped by a specified multiple, then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image. Finally, data features are extracted and classification is given by utilizing a 12-layer convolution neural network. Experiments of the proposed algorithm were carried out on CASIA-FASD. The results show that the classification accuracy is relatively high, and the half total error rate (HTER) reaches 1.02%.

人脸反欺骗是人脸识别系统中相对重要的部分，对于财务支付和访问控制系统具有重要意义。针对人脸对准不稳定，照明复杂，人脸防欺骗检测网络结构复杂的问题，提出了一种将卷积神经网络与亮度均衡相结合的新方法。首先，基于三级卷积神经网络（CNN）级联的多任务卷积神经网络（P-net，R-net和O-net）用于实现人脸和检测到的人脸的准确定位将边框按指定倍数裁剪，然后采用亮度均衡对面部图像的不同亮度区域进行亮度补偿。最后，利用12层卷积神经网络提取数据特征并进行分类。该算法在CASIA-FASD上进行了实验。结果表明，分类准确率较高，半总错误率（HTER）达到1.02％。