基于前向学习网络的人脸欺诈检测

Few-Shot Face Spoofing Detection Using Feedforward Learning Network

为了克服现有人脸欺诈检测方法在少样本应用场合下的局限性,将前向学习网络用于欺诈检测.通过前向学习的方式从图像中无监督地学得卷积滤波器,在人脸欺诈检测应用场合下,对前向学习网络进行了改进,改进后的网络使用了面向人脸欺诈检测任务的卷积滤波器.使用主成分分析变换所得的最小特征值对应的特征向量作为卷积滤波器提取图像的特征.将所提方法在CASIA-FASD、Idiap Replay-Attack和OULU-NPU数据集上进行了验证,实验结果表明,在少样本跨攻击类型实验中,所提方法显著提升了欺诈人脸检测的准确率.

In order to overcome the limitations of the existing face spoofing detection methods under fewshot face anti-spoofing applications,this paper proposes to use feedforward learning network for face antispoofing.The convolutional filters are learned unsupervisedly from the images in a feedforward manner.The feedforward learning network is adapted in the spoof face detection applications by using face antispoofing task-oriented convolutional filters learned from the training images.The eigenvectors that correspond to the smallest eigenvalues obtained from the principle component analysis transform are used as convolution filters for extracting features from images.The method is evaluated on some benchmark datasets including CASIA-FASD dataset,Idiap Replay-Attack dataset and OULU-NPU dataset.Experiments show that under the cross presentation attack detection experiments,the proposed method significantly improves the classification accuracy of existing methods.