상세정보
학술지
Improvement of Identity Recognition with Occlusion Detection-Based Feature Selection
- 발행일
- 202101
- 출처
- Electronics, v.10 no.2, pp.1-12
- ISSN
- 2079-9292
- 출판사
- MDPI
- 협약과제
- 20PS2400, 서비스 로봇의 사회적 상호작용을 위한 소셜 로봇지능 원천 기술 개발, 윤호섭
- 초록
- Image-based facial identity recognition has become a technology that is now used in many applications. This is because it is possible to use only a camera without the need for any other device. Besides, due to the advantage of contactless technology, it is one of the most popular certifications. However, a common recognition system is not possible if some of the face information is lost due to the user's posture or the wearing of masks, as caused by the recent prevalent disease. In some platforms, although performance is improved through incremental updates, it is still inconvenient and inaccurate. In this paper, we propose a method to respond more actively to these situations. First, we determine whether an obscurity occurs and improve the stability by calculating the feature vector using only a significant area when the obscurity occurs. By recycling the existing recognition model, without incurring little additional costs, the results of reducing the recognition performance drop in certain situations were confirmed. Using this technique, we confirmed a performance improvement of about 1~3% in a situation where some information is lost. Although the performance is not dramatically improved, it has the big advantage that it can improve recognition performance by utilizing existing systems.
- KSP 제안 키워드
- Feature Vector, Feature selection(FS), Identity Recognition, Image-based, Incremental update, Occlusion detection, Recognition System, Recognition model, Significant area, need for, performance improvement
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