基于多分类及特征融合的静默活体检测算法

现有的静默活体检测研究忽略不同非活体攻击方式之间的差异，以及不考虑活体和非活体样本类别不均衡对模型学习的不利影响. 本研究将非活体攻击类别细分成打印攻击和展示攻击，将静默活体检测由传统的二分类问题转变为多分类问题，并提出采取交叉熵作为损失函数对网络模型进行训练的方案，用以克服二分类和类别不均衡问题，使得模型训练中能更准确发现和抽象出非活体人脸样本共同的欺诈特征，提高网络模型对非活体识别的精准度. 构建双流特征融合网络模型，采取注意力机制对从RGB和YCrCb这2种不同色彩空间提取到的特征向量进行自适应加权融合，以进一步提升网络模型的特征表示能力. 在CASIA-FASD、Replay-Attack、MSU-MFSD和OULU-NPU 4个公开数据集进行大量的对比实验，实验结果表明，采取多分类策略以及特征融合的静默活体检测模型能够有效降低分类错误率并提升泛化能力.

Silent liveness detection algorithm based on multi classification and feature fusion network

Difference between non-liveness attack types is neglected, and adverse impact of category imbalance between liveness and non-liveness samples on model training is not considered in existing studies of silent liveness detection. In this paper, non-liveness attacks were subdivided into two categories, print attack and display attack, which transformed silent liveness detection from traditional two-classification problem into multi-classification problem. And the cross-entropy was used as the loss function to train network model. Thus, the disadvantage of binary classification and category imbalance can be eliminated, common features of the non-liveness face samples were likely to be identified more accurately through model training, and the accuracy of the network model was improved for non-liveness recognition. Moreover, a two-stream feature fusion the network model was constructed to further improve the feature representation capacity of the network model, which adopted the attention mechanism to adaptively fuse the feature vectors extracted from RGB and YCrCb. Abundant comparative experiments were performed on four public datasets, CASIA-FASD, Replay-Attack, MSU-MFSD and OULU-NPU. Experimental results indicate that silent liveness detection model adopting multi-classification strategy and feature fusion can effectively reduce the classification error and improve over-generalization ability.