基于多尺度双通道网络的人脸活体检测

Face Liveness Detection Based on Multi-Scale Dual Channel Network

人脸活体检测在人脸识别系统的安全保护中发挥着重要作用。现有基于频率域处理人脸活体检测问题的方法是从高频信息中提取边缘和纹理信息,进而获取伪造痕迹特征,但是频域方法对光照环境和传感器采集设备变化的适应性差,鲁棒性较差。针对该问题,提出了基于多尺度双通道神经网络的人脸活体检测方法,构建了频率域通道和空间域通道,分别从频率域图像和RGB图像中提取多尺度频率域特征和多尺度空间域特征,并采用注意力机制进行双通道的特征融合,增强了网络的特征提取能力。与同类方法相比,本文方法在Oulu-NPU和Siw数据集上的检测错误率最低,并且在Idiap Replay Attack数据集上的准确率可达99%以上,验证了本文所提出的多尺度双通道网络的有效性和鲁棒性。

Face liveness detection plays an important role in the security of face recognition systems.The existing method based on frequency domain to deal with the problem of face liveness detection is to extract edge and texture information from high-frequency information,and then obtain forged trace features.However,the frequency domain method has poor adaptability to the change of lighting environment and sensor acquisition equipment,and its robustness is poor.To solve this problem,a method of face liveness detection based on multi-scale dual channel neural network was proposed.Frequency domain channel and spatial domain channel were constructed.Multi-scale frequency domain features were multi-scale spatial domain features were extracted from frequency domain images and RGB images respectively.Attention mechanism was used for dual channel feature fusion to enhance the feature extraction capability of the network.Compared with similar methods,the proposed method has the lowest detection error rate on the Oulu-NPU and Siw datasets,and the accuracy rate can reach more than 99%on the Idiap Replay Attack dataset,which verifies the effectiveness and robustness of the multi-scale dual channel network proposed in this paper.