Abstract:

To resist recaptured image's attack towards face recognition system, an algorithm based on predicting face image's gradient direction was proposed. The contrast of real image and recaptured image was enhanced by adaptive Gauss homomorphic's illumination compensation. A Support Vector Machine (SVM) classifier was chosen for training and testing two kinds of pictures with convoluting 8-direction Sobel operator. Using 522 live and recaptured faces come from domestic and foreign face databases including NUAA Imposter Database and Yale Face Database for experiment, the detection rate reached 99.51%; Taking 261 live face photos using Samsung Galaxy Nexus phone, then remaked them to get 522 samples library, the detection rate was 98.08% and the time of feature extraction was 167.04s. The results show that the proposed algorithm can classify live and recaptured faces with high extraction efficiency.

摘要：

为抵抗翻拍图像对人脸识别等认证系统的攻击，提出一种人脸图像梯度方向预测算法。通过自适应高斯同态滤波进行光照补偿增强真实活体图像与翻拍图像的对比度，用八方向Sobel算子与像元卷积方向预测，并使用支持向量机(SVM)分类器设计图像分类器判别两类图像。抽取国内外数据库(南京航空航天大学与耶鲁大学人脸库)活体人脸与翻拍人脸共522张进行实验，检测率达到99.51%；另用三星Galaxy Nexus手机拍摄261张真实人脸，同时进行翻拍，得到样本库522张人脸，实验检测率达到98.08%，特征提取用时167.04s。结果表明能有效地检测分类出真实人脸照片与翻拍假冒照片，并具有较高的特征提取效率。