Partial occlusion face recognition based on structured occlusion coding and extreme learning machine

doi:10.11772/j.issn.1001-9081.2019051176

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (10): 2893-2898.DOI: 10.11772/j.issn.1001-9081.2019051176

• Artificial intelligence • Previous Articles Next Articles

Partial occlusion face recognition based on structured occlusion coding and extreme learning machine

ZHANG Fangyan^1,2, WANG Xin¹, XU Xinzheng^1,2

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 221116, China;
2. Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education(Lanzhou Jiaotong University), Lanzhou Gansu 730070, China

Received:2019-05-24 Revised:2019-07-08 Online:2019-10-10 Published:2019-09-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61976217), the Opening Project of Key Laboratory of Opto-Technology and Intelligent Control, Ministry of Education (KFKT2018-3).

基于结构化遮挡编码和极限学习机的局部遮挡人脸识别

张芳艳^1,2, 王新¹, 许新征^1,2

1. 中国矿业大学计算机科学与技术学院, 江苏徐州 221116;
2. 光电技术与智能控制教育部重点实验室(兰州交通大学), 兰州 730070

通讯作者: 许新征
作者简介:张芳艳(1990-),女,甘肃宁县人,硕士研究生,主要研究方向:深度学习;王新(1978-),女,山东蒙阴人,副教授,博士,CCF会员,主要研究方向:机器学习;许新征(1980-),男,安徽宿州人,副教授,博士,CCF高级会员,主要研究方向:机器学习、模式识别。
基金资助:
国家自然科学基金资助项目（61976217）；光电技术与智能控制教育部重点实验室开放课题（KFKT2018-3）。

Abstract

Abstract: An algorithm combining Structured Occlusion Coding (SOC) with Extreme Learning Machine (ELM) was proposed to deal with the occlusion problem in face recognition. Firstly, the SOC was used to remove the occlusion from the image and separate the oclusion from the human face. At the same time, the position of the occlusion was estimated by the Local Constraint Dictionary (LCD), and an occlusion dictionary and a face dictionary were established. Then, the established face dictionary matrix was normalized, and the ELM was used to classify and identify the normalized data. Finally, the simulation results on the AR face database show that the proposed method has higher recognition rate and stronger robustness for different types of occlusions and images with different regions occluded.

Key words: face recognition, occlusion, Structured Occlusion Coding (SOC), local constraint dictionary, Extreme Learning Machine (ELM)

摘要： 提出使用结构化遮挡编码（SOC）结合极限学习机（ELM）的算法来处理人脸识别中的遮挡问题。首先，使用SOC去除图像上的遮挡物，将遮挡物体与人脸分离开；同时，通过局部性约束字典（LCD）来估计遮挡物的位置，建立遮挡字典和人脸字典。然后，将建立好的人脸字典矩阵进行归一化处理，并利用ELM对归一化的数据进行分类识别。最后，在AR人脸库上进行的仿真实验结果表明，所提方法对不同遮挡物和不同区域遮挡的图像具有较好的识别率和鲁棒性。

关键词: 人脸识别, 遮挡, 结构化遮挡编码, 局部性约束字典, 极限学习机

CLC Number:

TP193

ZHANG Fangyan, WANG Xin, XU Xinzheng. Partial occlusion face recognition based on structured occlusion coding and extreme learning machine[J]. Journal of Computer Applications, 2019, 39(10): 2893-2898.

张芳艳, 王新, 许新征. 基于结构化遮挡编码和极限学习机的局部遮挡人脸识别[J]. 计算机应用, 2019, 39(10): 2893-2898.

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Partial occlusion face recognition based on structured occlusion coding and extreme learning machine

基于结构化遮挡编码和极限学习机的局部遮挡人脸识别

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