基于深度学习和特征融合的人脸活体检测算法

针对目前基于深度学习的活体检测算法大都基于大型卷积神经网络的问题，提出一种基于轻量级网络MobileNetV2和特征融合的活体检测算法。首先，以改进的MobileNetV2为基础网络分别从RGB、HSV、LBP图中提取特征；然后，将得到的特征图堆叠在一起以进行特征层的融合；最后，从融合后的特征图中继续提取特征，并利用Softmax层作出真假人脸的判断。仿真结果显示，所提算法在NUAA数据集上的等错误率（EER）为0.02%，在Siw数据集上的ACER（Average Classification Error Rate）为0.75%，而且测试单张图像仅用时6 ms。实验结果表明：融合不同的信息可以获得更低的错误率，改进的轻量化网络保证了算法的高效性并满足实时性需求。

Face liveness detection algorithm based on deep learning and feature fusion

Aiming at the problem that the existing liveness detection algorithms based on deep learning are mostly based on large convolutional neural network,a liveness detection algorithm based on lightweight network MobileNetV2 and feature fusion was proposed. Firstly,the improved MobileNetV2 was used as the basic network to extract features from RGB,HSV and LBP images respectively. Then,the obtained feature maps were stacked together to perform the feature layer fusion. Finally,the features were extracted from the merged feature maps,and the Softmax layer was used to make the judgment whether the face was real or fake. Simulation results show that the Equal Error Rate(EER)of the proposed algorithm on NUAA dataset was 0. 02%,the Average Classification Error Rate(ACER)on Siw dataset was 0. 75%,and the time to test single image costed 6 ms. Experimental results verify that:the fusion of different information can obtain a lower error rate, and the improved lightweight network guarantees the efficiency of the algorithm and meets the real-time requirement.