基于多对抗性鉴别网络的人脸活体检测

Face liveness detection based on multi-adversarial discrimination network

人脸活体检测是人脸识别系统安全性保证的关键,其中,解纠缠学习方法可以有效解决人脸活体检测中泛化数据集的问题,但是现有的解纠缠学习方法往往将整幅人脸图像作为输入,解析出伪造痕迹元素,会忽略伪造痕迹的局部细节问题。针对这一问题,改进现有的伪造痕迹解纠缠网络,提出多对抗性鉴别网络模型,在鉴别器中设计主鉴别器和区域鉴别器,引入人脸遮罩模块,生成人脸皮肤、五官遮罩蒙版,整合人脸局部信息,使生成器拟合的图像更接近数据集中人脸图像的分布,同时解离出加强版的伪造痕迹。提出的多对抗性鉴别网络有效地增强了伪造人脸图像的伪造痕迹信息并提高了人脸活体检测的准确率。具体来说,该网络模型在OULU-NPU数据集的2个实验中的检测错误率仅为0.8%和1.4%,相比STDN错误率显著降低,同时在Idiap Replay-Attack数据集上也达到了较好的检测效果。为了验证该网络模型的可迁移性,在NUAA数据集和Idiap Replay-Attack数据集上进行跨域实验,达到了不错的效果。

Face liveness detection is a key factor in ensuring the security of face recognition systems.In particular,disentangled learning methods can effectively address the problem of generalizing datasets in face liveness detection.However,existing disentangled learning methods often take the entire face image as input and parse out forged trace elements,ignoring the issue of local details of forged traces.To address this issue,this paper improves the existing forgery trace disentanglement network and proposes a multi-adversarial discriminative network model.The discriminator is designed with a primary discriminator and a regional discriminator.A facial mask module is introduced to generate facial skin and feature masks.Local facial information is integrated to make the generated images more closely resemble the distribution of face images in the dataset,while also disentangling an enhanced version of the forgery trace.The proposed multi-adversarial discriminative network effectively enhances the effect of forgery trace on forged face images and improves the accuracy of face liveness detection.Specifically,the detection error rate of our model on the OULU-NPU dataset in two experiments is only 0.8%and 1.4%,significantly lower than that of the STDN.At the same time,good detection results are achieved on the Idiap Replay-Attack dataset.To verify the transferability of our method,cross-domain experiments on the NUAA dataset and the Idiap Replay-Attack dataset also achieves good results.