基于InceptionV3和特征融合的人脸活体检测

Face liveness detection based on InceptionV3 and feature fusion

针对身份验证中经常出现的照片欺诈问题,提出了一种基于InceptionV3和特征融合的人脸活体检测模型——InceptionV3_FF。首先,在ImageNet数据集上预训练InceptionV3模型;其次,从InceptionV3模型的不同层得到图像的浅层、中层和深层特征;然后,将不同的特征进行融合得到最终的特征;最后,使用全连接层对特征进行分类,从而实现端到端的训练。InceptionV3_FF模型在NUAA数据集和自制的STAR数据集上进行仿真实验,实验结果表明,InceptionV3_FF模型在NUAA数据集和STAR数据集上分别取得了99.96%和98.85%的准确率,高于InceptionV3迁移学习和迁移微调模型;而与非线性扩散卷积神经网络(ND-CNN)、扩散核(DK)、异构内核卷积神经网络(HKCNN)等模型相比,InceptionV3_FF模型在NUAA数据集上的准确率更高,具备一定的优越性。InceptionV3_FF模型对数据集中随机抽取的单张图片进行识别时,仅需4 ms。InceptionV3_FF模型和OpenCV结合构成的活体检测系统可以对真假人脸进行识别。

Aiming at the photo spoofing problem that often occurs in identity verification,a face liveness detection model based on InceptionV3 and feature fusion,called InceptionV3 and Feature Fusion(InceptionV3_FF),was proposed.Firstly,the InceptionV3 model was pretrained on ImageNet dataset.Secondly,the shallow,middle,and deep features of the image were obtained from different layers of the InceptionV3 model.Thirdly,different features were fused to obtain the final features.Finally,the fully connected layer was used to classify the features to achieve end-to-end training.The InceptionV3_FF model was simulated on NUAA dataset and self-made STAR dataset.Experimental results show that the proposed InceptionV3_FF model achieves the accuracy of 99.96%and 98.85%on NUAA dataset and STAR dataset respectively,which are higher than those of the InceptionV3 transfer learning and transfer fine-tuning models.Compared with Nonlinear Diffusion-CNN(ND-CNN),Diffusion Kernel(DK),Heterogeneous Kernel-Convolutional Neural Network(HK-CNN)and other models,the InceptionV3_FF model has higher accuracy on NUAA dataset and has certain advantages.When the InceptionV3_FF model recognizes a single image randomly selected from the dataset,it only takes 4 ms.The face liveness detection system consisted of the InceptionV3_FF model and OpenCV can identify real and fake faces.