基于注意力融合的多模态人脸活体检测算法

Attention-Based Multimodal Fusion for Face Anti-spoofing

针对人脸活体检测技术在复杂光线条件下识别率低,边缘设备硬件条件有限的问题,文章提出一种基于注意力机制融合的多模态人脸活体检测算法。将可见光图的PLGF特征图和近红外图作为输入,通过注意力机制进行融合,并采用颜色通道差值图进行辅助判定。设计了轻量化骨干网络提升算法在边缘设备中的推理效率,参数量相比mobelienetV2减少80%。在自建数据集上的实验表明,人脸活体检测准确率为99.93%,可有效提升算法在不同攻击方式、不同光照条件下的准确率。

In order to solve the problems of low recognition rate of face anti-spoofing technology in complex light conditions and limited hardware conditions of edge equipment,this paper proposes a Attention-Based Multimodal Fusion for face anti-spoofing.The PLGF characteristic image and near-infrared image of the visible light image are taken as the input,and the fusion is carried out through the attention mechanism,and the color channel difference image is used for auxiliary judgment.The reasoning efficiency of lightweight backbone network promotion algorithm in edge devices is designed,and the number of parameters is reduced by 80% compared with mobelinetV2.The experiment on the self built dataset shows that the accuracy rate of face anti-spoofing is 99.93%,which can effectively improve the accuracy of the algorithm under different attack modes and different lighting conditions.