Lightweight and multi-pose face recognition method based on deep learning

doi:10.11772/j.issn.1001-9081.2019071272

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 704-709.DOI: 10.11772/j.issn.1001-9081.2019071272

• Artificial intelligence • Previous Articles Next Articles

Lightweight and multi-pose face recognition method based on deep learning

GONG Rui^1,2, DING Sheng^1,2,3, ZHANG Chaohua^1,2, SU Hao^1,2

1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
2. Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial System(Wuhan University of Science and Technology), Wuhan Hubei 430065, China;
3. Fujian Provincial Key Laboratory of Data Intensive Computing(Quanzhou Normal University), Quanzhou Fujian 362000, China

Received:2019-07-22 Revised:2019-09-26 Online:2020-03-10 Published:2019-10-25
Supported by:
This work is partially supported by the Open Project of the Fujian Provincial Key Laboratory of Data Intensive Computing (BD201805).

基于深度学习的轻量级和多姿态人脸识别方法

龚锐^1,2, 丁胜^1,2,3, 章超华^1,2, 苏浩^1,2

1. 武汉科技大学计算机科学与技术学院, 武汉 430065;
2. 智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学), 武汉 430065;
3. 福建省大数据管理新技术与知识工程重点实验室(泉州师范大学), 福建泉州 362000

通讯作者: 龚锐
作者简介:龚锐(1995-),男,湖北赤壁人,硕士研究生,主要研究方向:深度学习、计算机视觉;丁胜(1975-),男,湖北武汉人,副教授,博士,CCF会员,主要研究方向:计算机视觉;章超华(1996-),男,江西抚州人,硕士研究生,主要研究方向:深度学习、计算机视觉;苏浩(1996-),男,湖北武汉人,硕士研究生,主要研究方向:迁移学习、计算机视觉。
基金资助:
福建省大数据管理新技术与知识工程重点实验室开放课题（BD201805）。

Abstract

Abstract: At present, the face recognition methods based on deep learning have the problems of large model parameter size and slow feature extraction speed, and the existing face datasets have the problem of single pose, which cannot achieve good recognition effect in the actual face recognition task. Aiming at this problem, a multi-pose face dataset was established, and a lightweight multi-pose face recognition method was proposed. Firstly, the MTCNN (Multi-Task cascaded Convolutional Neural Network) algorithm was used by the method for face detection, and the high-level features included in the last network of MTCNN were used for face tracking. Then, the face pose was judged according to the positions of the detected face key points, the current face features were extracted by the neural network with ArcFace as loss function, and the current face features were compared with the face features of the corresponding pose in the face database to obtain the face recognition result. The experimental results show that the accuracy of the proposed method is 96.25% on the multi-pose face dataset, which is 2.67% higher than that on the face dataset with single pose. It shows that the proposed multi-pose face recognition method can effectively improve the recognition accuracy.

Key words: deep learning, face recognition, multi-pose, lightweight, Multi-Task cascaded Convolutional Neural Network (MTCNN), ArcFace

摘要： 目前基于深度学习的人脸识别方法存在识别模型参数量大、特征提取速度慢的问题，而且现有人脸数据集姿态单一，在实际人脸识别任务中无法取得好的识别效果。针对这一问题建立了一种多姿态人脸数据集，并提出了一种轻量级的多姿态人脸识别方法。首先，使用多任务级联卷积神经网络（MTCNN）算法进行人脸检测，并且使用MTCNN最后包含的高层特征做人脸跟踪；然后，根据检测到的人脸关键点位置来判断人脸姿态，通过损失函数为ArcFace的神经网络提取当前人脸特征，并将当前人脸特征与相应姿态的人脸数据库中的人脸特征比对得到人脸识别结果。实验结果表明，提出方法在多姿态人脸数据集上准确率为96.25%，相较于单一姿态的人脸数据集，准确率提升了2.67%，所提方法能够有效提高识别准确率。

关键词: 深度学习, 人脸识别, 多姿态, 轻量级, 多任务级联卷积神经网络, ArcFace

CLC Number:

TP183

GONG Rui, DING Sheng, ZHANG Chaohua, SU Hao. Lightweight and multi-pose face recognition method based on deep learning[J]. Journal of Computer Applications, 2020, 40(3): 704-709.

龚锐, 丁胜, 章超华, 苏浩. 基于深度学习的轻量级和多姿态人脸识别方法[J]. 计算机应用, 2020, 40(3): 704-709.

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Lightweight and multi-pose face recognition method based on deep learning

基于深度学习的轻量级和多姿态人脸识别方法

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