The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.

中文翻译：

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基于带有通道注意机制的残差网络的人脸反欺骗方法

人脸识别系统容易受到有效用户面部照片或视频的欺骗攻击。然而，当使用非活体人脸图像攻击人脸识别系统时，会出现边缘退化和纹理模糊。考虑到这一点，一种新颖的人脸反欺骗方法结合了残差网络和通道注意机制。在我们的方法中，残差网络提取人脸图像之间特征的纹理差异。相比之下，注意力机制侧重于活体和非活体面部图像之间位于鼻部和脸颊区域的阴影和边缘特征的差异。它可以为人脸图像的不同滤波特征分配权重，增强网络提取和表达鼻部和脸颊区域不同关键特征的能力，提高检测精度。实验是在 Replay-Attack 和 CASIA-FASD 的公众人脸反欺骗数据集上进行的。我们找到了参数的最佳值适用于人脸反欺骗研究的r为16，该方法的准确率分别为99.98%和97.75%。此外，为了增强该方法对光照变化的鲁棒性，我们还在光照变化的数据集上进行了实验，取得了良好的效果。