THE DEVELOPMENT OF FACE RECOGNITION MODEL IN INDONESIA PANDEMIC CONTEXT BASED ON DCNN AND ARCFACE LOSS FUNCTION

IF 1.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Innovative Computing Information and Control Pub Date : 2021-01-01 DOI:10.24507/ijicic.17.05.1513
Mauritsius T. Wirianto
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引用次数: 2

Abstract

The advancement of technology opens opportunities for implementation that benefits the social and economic aspects of human life. Given the latest achievement in face recognition technology that surpasses human ability to identify a face, the research explores the application of this scientific discovery in the Indonesian context during the current pandemic situation. Toward the effort to achieve this goal, the study develops an Indonesia Labelled Face in the Wild (ILFW) that collects face images of famous Indonesian people from the Internet in various poses, expressions, lighting/illumination, and fashion attribute. In response to the recent COVID-19 pandemic situation, the study also augmented a face mask to a portion of collected face images. Using DCNN, RetinaFace as the face detection model, and Arcface loss function, and adopting CRISP DM, the research contributes by providing a method to develop a face dataset with 1,200 identities, and face recognition model with 92 percent accuracy and be able to recognize Indonesian people with a face mask. The researchers also recommend use cases for realtime face recognition in the business organization. It uses CCTV to perform automatic attendance, security surveillance, and employee location tracking and exhibits deployment consideration. Future research could increase the accuracy of face recognition model by adding more identities to the face dataset.
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基于DCNN和弧面损失函数的印尼大流行背景下人脸识别模型的开发
技术的进步为实施提供了机会,使人类生活的社会和经济方面受益。鉴于人脸识别技术的最新成就超越了人类识别人脸的能力,本研究探讨了这一科学发现在当前疫情背景下在印度尼西亚的应用。为了实现这一目标,该研究开发了一个印度尼西亚野外标签脸(ILFW),它从互联网上收集了印度尼西亚名人的各种姿势、表情、灯光/照明和时尚属性的面部图像。针对最近的COVID-19大流行情况,该研究还在收集的部分面部图像上增加了口罩。本研究利用DCNN、retaface作为人脸检测模型和Arcface损失函数,采用CRISP DM,开发了包含1200个身份的人脸数据集,人脸识别模型准确率达到92%,能够识别带口罩的印尼人。研究人员还推荐了在商业组织中进行实时人脸识别的用例。它使用闭路电视来执行自动考勤、安全监视和员工位置跟踪,并展示部署考虑。未来的研究可以通过在人脸数据集中添加更多的身份来提高人脸识别模型的准确性。
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来源期刊
CiteScore
3.20
自引率
20.00%
发文量
0
审稿时长
4.3 months
期刊介绍: The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly
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