학습기반 효율적인 얼굴 검출 시스템 설계
Design of an efficient learning-based face detection system
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.
Abstract
Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.
참고문헌 (11)
- 김선집, "공공 IaaS 클라우드 인증제도에 적용할 위험분석 방법에 대한 연구", 융합보안 논문지, 15(5), pp.9~15 (2015), 한국융합보안학회
- 고석하, "프로그램 관리 관점에 기반을 둔 소프트웨어 아키텍처 생애주기 모델 : 확장된 나선형 모델", Journal of Information Technology Applications & Management, 20(2), pp.69~87 (2013), 한국데이터전략학회
- Taigman. Y, "Closing the gap to human-level performance in face verification", Proceedings IEEE conference on computer vision and pattern recognition, pp.1701~1708 (2014)
- 박병준, "눈 검출을 이용한 얼굴인식 알고리즘에 관한 연구", 한국정보전자통신기술학회 논문지, 8(6), pp.491~496 (2015), 한국정보전자통신기술학회
- Neto. J. B. C, "3DLBP and HAOG Fusion for Face Recognition Utilizing Kinect as a 3D Scanner", Proceeding of the 30th Annual ACM Symposium on Applied Computing, pp.66~73 (2015), ACM
- Zhang. K, "Joint Face Detection and Alignment using Multitask Cascaded Convolutional Networks", IEEE Conference on Computer Vision and Pattern Recognition, 23(10), pp.1499~1503 (2016)
- 윤경신, "얼굴인식 성능 향상을 위한 얼굴 전역 및 지역 특징 기반 앙상블 압축 심층합성곱신경망 모델 제안", 멀티미디어학회논문지, 23(8), pp.1019~1029 (2020), 한국멀티미디어학회
- Zubaida Said Ameen, "An improved CNN-LSTM deep model for Classification of guideRNA in CRISPR-Casl2System", International Conference on Artificial Intelligence in Everything (2022)
- 박병준, "얼굴 특징을 이용한 얼굴영역 검출에 관한 연구", 한국정보전자통신기술학회 논문지, 13(3), pp.206~211 (2020), 한국정보전자통신기술학회
- 김연호, "CNN을 적용한 조명변화에 강인한 얼굴인식 연구", 한국전자통신학회 논문지, 12(2), pp.325~330 (2017), 한국전자통신학회[DOI]
- 이요섭, "딥 러닝 프레임워크의 비교 및 분석", 한국전자통신학회 논문지, 12(1), pp.115~122 (2017), 한국전자통신학회[DOI]