跨域人脸活体检测的单边对抗网络算法

doi:10.3778/j.issn.1002-8331.2210-0134

摘要/Abstract

摘要： 现有跨域人脸活体检测算法，其特征提取过程容易发生过拟合和缺乏特征聚合所导致的泛化性不足问题。针对该问题，提出了跨域人脸活体检测的单边对抗网络算法，将分组卷积与改进的倒残差结构融合替换普通卷积，降低网络参数同时加强人脸细粒度特征的表达能力，并引入自适应特征归一化模块，强调图像中人脸活体信息区域淡化无关背景区域，有效避免人脸活体信息的过拟合并加强来自不同源域的人脸活体检测能力。基于NetVLAD引入通道注意力机制模块，通道注意力机制模块作为特征聚合网络的分支，学习不同源域中人脸局部特征的语义信息，有效增强对不同源域的人脸活体信息分类的泛化能力。设计两模块融合网络以提高未知场景下跨域人脸活体检测精度。在OULU-NPU、CASIA-FASD、MSU-MFSD和Idiap Replay-Attack数据集上的实验结果表明，该算法在跨数据集测试O&C&M to I、O&C&I to M、I&C&M to O、O&M&I to C均有不错的表现，其中，在O&C&I to M及O&M&I to C性能评估指标分别提升了0.99个百分点和0.5个百分点的精度。

关键词: 域泛化, 生成对抗网络, 人脸活体检测, 自适应归一化, 注意力机制

Abstract: In the existing cross-domain face detection algorithms, the feature extraction process is prone to overfitting and lack of feature aggregation, resulting in insufficient generalization. To solve this problem, this paper proposes a unilateral adversarial network algorithm for cross-domain face in vivo detection. Firstly, grouping convolution and improved reciprocal residual structure are fused to replace ordinary convolution to reduce network parameters and enhance the expression ability of face fine-grained features, and an adaptive feature normalization module is introduced, emphasizing the face in vivo information region fade irrelevant background region in the image. Effectively it avoids the overfitting merging of live face information and enhances the ability of face detection from different source domains. Secondly, based on NetVLAD, the channel attention mechanism module is introduced. As a branch of feature aggregation network, the channel attention mechanism module learns the semantic information of local features in different source domains, effectively enhancing the generalization ability of face live information classification in different source domains. Finally, a two-module fusion network is designed to improve the accuracy of cross-domain face detection in unknown scenes. Experimental results on OULU-NPU, CASIA-FASD, MSU-MFSD, and Idiap Replay-Attack data sets show that, the proposes algorithm has good performance in cross-data set tests of O&C&M to I, O&C&I to M, I&C&M to O, and O&M&I to C. Among them, the performance evaluation indexes of O&C&I to M and O&M&I to C have improved the accuracy by 0.99 percentage points and 0.5 percentage points respectively.

Key words: domain generalization, generative adversarial networks, face liveness detection, adaptive normalization, attention mechanism

曾凡智, 吴楚涛, 周燕. 跨域人脸活体检测的单边对抗网络算法[J]. 计算机工程与应用, 2024, 60(5): 103-111.

ZENG Fanzhi, WU Chutao, ZHOU Yan. Cross-Domain Face in Vivo Detection of Unilateral Adversarial Network Algorithm[J]. Computer Engineering and Applications, 2024, 60(5): 103-111.

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