Cross-Domain Face in Vivo Detection of Unilateral Adversarial Network Algorithm

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. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.