摘要
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%.
人脸活体检测是人脸识别系统中比较重要的一环,对金融支付、门禁系统等具有重大意义。针对人脸对齐不稳定、复杂光照、活体检测网络结构复杂等问题,论文提出使用卷积神经网络和亮度均衡结合的方法。论文首先使用基于P-net,R-net,O-net三个CNN进行级联的MTCNN算法,实现对人脸的精准定位并将检测出的人脸边界框按指定倍数裁剪人脸,接下来使用亮度均衡对人脸图像不同亮度区域进行亮度补偿,最后使用一个设计的12层卷积神经网络提取数据特征并进行分类。论文将所提算法在CASIA-FASD上进行实验,结果表明分类准确率比较高,HTER达到了1.02%。
作者
CAI Pei
QUAN Hui-min
蔡佩;全惠敏(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)
基金
Project(61671204)supported by National Natural Science Foundation of China
Project(2016WK2001)supported by Hunan Provincial Key R&D Plan,China。
