基于真实人脸类别对抗机制的人脸活体检测算法

Face Liveness Detection Algorithm Based on Real Face Category Adversarial Mechanism

针对现有人脸活体检测算法在单一数据集内表现良好而在多个数据集间泛化能力较差的问题,提出一种聚焦于真实人脸的活体检测方法。在数据输入阶段,每轮训练会向网络输入所有源域的真实人脸的同时只随机输入其中一个源域的虚假人脸。在特征学习阶段,使用Resnet18网络作为主干网络,对不同残差块的输出特征进行基于注意力机制的加权融合。利用三元组损失和对抗损失对融合后的真实人脸特征进行领域内和领域间的聚合,利用三元组损失对融合后的虚假人脸特征只进行领域内的聚合。在分类阶段,利用交叉熵损失对所有源域的真实人脸和虚假人脸进行分类。所提方法在4个人脸活体检测数据集中进行了实验,实验结果表明所提方法相比其他方法具有更低的识别错误率和更高的鲁棒性。

Given that existing face liveness detection algorithms perform well in a single data set but have poor generalization ability in cross multiple data sets;therefore,this study proposes a liveness detection method centering on real faces.During the data input stage,each round of training will input the real faces of multiple source domains into the network,while only randomly input false faces of one source domain.During the feature learning stage,Resnet18 serves as the backbone network to weight fuse the output features of different residual blocks based on the attention mechanism.Triple loss and adversarial loss are used to aggregate the fused real face features within each domain and cross domains,while triplet loss is used to aggregate the fused fake face features within each domain.During the classification stage,crossentropy loss is used to classify real and false faces in all source domains.The proposed method was tested on four live face detection data sets,and the experimental results reveal that the proposed method has a lower recognition error rate and higher robustness than other methods.