基于深度学习的多模态融合三维人脸识别  

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

作　　者：胡乃平[1] 贾浩杰 HU Nai-Ping;JIA Hao-Jie(School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2022年第8期152-159,共8页Computer Systems & Applications

摘　　要：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.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.

关 键 词：三维人脸识别 多模态融合 深度学习 卷积神经网络 损失函数 迁移学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]