一种高效且轻量的RGB单帧人脸反欺诈模型

An efficient and lightweight RGB frame-level face anti-spoofing model

针对在仅具有三原色(red-green-blue,RGB)摄像头的通用消费设备上部署基于深度学习的人脸反欺诈(face anti-spoofing,FAS)算法时存在的挑战问题,提出一种高效且轻量的RGB单帧FAS(efficient and lightweight RGB frame-level face anti-spoofing,EL-FAS)模型。探索一种新的全局空间自注意力机制捕获全局上下文信息的依赖关系,以提高模型泛化能力并在受限条件下实现高检测性能;设计一种等通道像素级二元监督方法,强制模型从不同的像素中学习共享特征;采用Bottleneck模块搭建骨干网络以减少模型参数。试验结果表明,EL-FAS模型在OULU-NPU数据集的大多数协议上平均分类错误率R_(ACE)最低,取得较好的人脸欺诈检测效果,在SiW数据集和跨数据集测试中也取得较好的性能,并且模型轻量,参数只有1.34×10^(6)个。

Based on the challenge when deploying a deep learning-based face anti-spoofing(FAS)algorithm on general-purpose consumer devices with only RGB camera,an efficient and lightweight RGB frame-level FAS model(EL-FAS)was proposed.To improve the generalization ability of the model and achieve high performance under constrained conditions,a novel global spatial self-attention mechanism was explored to capture global feature dependencies,and an equal-channel pixel-wise binary supervision method was designed to force our model to learn shared features from different pixels.The Bottlenecks residual block was used to establish the backbone network to reduce parameters.Analysis and the experimental results showed that EL-FAS model achieved state-of-the-art performance in the OULU-NPU dataset,obtained competitive performance in the SiW dataset and cross-dataset tests.The model was lightweight,with only 1.34×10^(6) parameters.