基于深度学习的多模态融合三维人脸识别

二维人脸识别受光照、遮挡和姿态的影响较大. 为了克服二维人脸识别的缺点, 本文提出了一种基于深度学习的多模态融合三维人脸识别算法. 该方法首先使用卷积自编码器将彩色图像和深度图进行融合, 将融合后的图像作为网络的输入进行预训练, 并且设计了一种新的损失函数cluster loss, 结合Softmax损失, 预训练了一个精度非常高的模型. 之后使用迁移学习将预训练的模型进行微调, 得到了一个轻量级神经网络模型. 将原始数据集进行一系列处理, 使用处理之后的数据集作为测试集, 测试的识别准确率为96.37%. 实验证明, 该方法弥补了二维人脸识别的一些缺点, 受光照和遮挡的影响非常小, 并且相对于使用高精度三维人脸图像的三维人脸识别, 本文提出的算法速度快, 并且鲁棒性高.

3D Face Recognition with Multi-modal Fusion Based on Deep Learning

Two-dimensional (2D) face recognition is greatly affected by illumination, occlusion, and attitude. To overcome these shortcomings, this study proposes a 3D face recognition algorithm with multi-modal fusion based on deep learning. Firstly, the convolutional autoencoder fuses the color image and the depth map, and the fused image is input to the network for pre-training. In addition, a new loss function cluster loss is designed for pre-training in combination with the Softmax loss, so as to obtain a highly accurate model. Then, transfer learning is employed to fine-tune the pre-trained model, and thus a lightweight neural network model is obtained. The processed original dataset is used as the test set, and the identification accuracy of the test reaches 96.37%. Experimental results verify that the proposed method makes up for some shortcomings of 2D face recognition, and it is less affected by illumination and occlusion. Compared with 3D face recognition using high-precision 3D face images, the proposed algorithm is faster and more robust.