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首页> 外文会议>2018 13th IEEE International Conference on Automatic Face amp; Gesture Recognition >Deep Transfer Network with 3D Morphable Models for Face Recognition
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Deep Transfer Network with 3D Morphable Models for Face Recognition

机译：具有3D可变形模型的人脸识别深层传输网络

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摘要

Data augmentation using 3D face models to synthesize faces has been demonstrated to be effective for face recognition. However, the model directly trained by using the synthesized faces together with the original real faces is not optimal. In this paper, we propose a novel approach that uses a deep transfer network (DTN) with 3D morphable models (3DMMs) for face recognition to overcome the shortage of labeled face images and the dataset bias between synthesized images and corresponding real images. We first utilize the 3DMM to synthesize faces with various poses to augment the training dataset. Then, we train a deep neural network using the synthesized face images and the original real face images. The results obtained on LFW show that the accuracy of the model utilizing synthesized data only is lower than that of the model using the original data, although the synthesized dataset contains much considerably images with more unconstrained poses. This result shows that a dataset bias exists between the synthesized faces and the real faces. We treat the synthesized faces as the source domain, and we treat the actual faces as the target domain. We use the DTN to alleviate the discrepancy between the source domain and the target domain. The DTN attempts to project source domain samples and target domain samples to a new space where they are fused together such that one cannot distinguish the domain from which a specific image is from. We optimize our DTN based on the maximum mean discrepancy (MMD) of the shared feature extraction layers and the discrimination layers. We choose AlexNet and Inception-ResNet-V1 as our benchmark models. The proposed method is also evaluated on the LFW and SLLFW databases. The experimental results show that our method can effectively address the domain discrepancy. Moreover, the dataset bias between the synthesized data and the real data is remarkably reduced, which can thus improve the performance of the convolutional neural network (CNN) model.
机译：使用3D人脸模型来合成人脸的数据增强已被证明对人脸识别有效。但是，通过使用合成人脸和原始真实人脸直接训练的模型不是最佳的。在本文中，我们提出了一种新颖的方法，该方法使用具有3D可变形模型（3DMM）的深度传输网络（DTN）进行人脸识别，从而克服了标记人脸图像的不足以及合成图像与相应真实图像之间的数据集偏差。我们首先利用3DMM来合成具有各种姿势的面部，以增强训练数据集。然后，我们使用合成的人脸图像和原始的真实人脸图像训练深度神经网络。在LFW上获得的结果表明，仅使用合成数据的模型的准确性低于使用原始数据的模型的准确性，尽管合成数据集包含相当多的具有更多不受约束姿势的图像。该结果表明，在合成人脸和真实人脸之间存在数据集偏差。我们将合成的人脸视为源域，将实际人脸作为目标域。我们使用DTN缓解源域和目标域之间的差异。 DTN尝试将源域样本和目标域样本投影到一个新的空间，在该空间中将它们融合在一起，以至于无法区分特定图像所来自的域。我们基于共享特征提取层和区分层的最大平均差异（MMD）优化DTN。我们选择AlexNet和Inception-ResNet-V1作为基准模型。还对LFW和SLLFW数据库进行了评估。实验结果表明，我们的方法可以有效地解决域差异。此外，合成数据和真实数据之间的数据集偏差显着降低，因此可以提高卷积神经网络（CNN）模型的性能。

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