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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.5
- 수록면
- 701 - 708 (8page)
이용수
초록· 키워드
오류제보하기
As one of the most widely used technology in the world right now, Face recognition has already received widespread attention by all the researcher and institutes. It has been used in many fields such as safety protection, surveillance system, crime control and even in our ordinary life such as home security and so on. This technology with today"s technology has advantages such as high connectivity and real time transformation. But we still need to improve its recognition rate, reaction time and also reduce impact of different environmental status to the whole system. So in this paper we proposed a face recognition system model with improved CNN which combining the characteristics of flat network and residual network, integrated learning, simplify network structure and enhance portability and also improve the recognition accuracy. We also used AR and ORL database to do the experiment and result shows higher recognition rate, efficiency and robustness for different image conditions.
목차
ABSTRACT
1. INTRODUCTION
2. IMPROVED MULTI-LAYER CONVOLUTIONAL NEURAL NETWORK MODEL
3. Improved CNN network combined with integrated learning
4. EXPERIMENT RESULT
5. CONCLUSION
REFERENCE
참고문헌 (12)
참고문헌 신청
R.Y. Zhang and E.J. Lee, “Face Recognition Based on Improved Convolutional Neural Network,” Proceeding of the Spring Conference of the Korea Multimedia Society, pp. 8-10, 2019.
Ｒ Chellappa, C L Wilson, and S Sirohey, “Human and machine recognition offaces: A survey,” Proceedings of the IEEE, Vol. 83, No. 5, pp. 705-740, 1995.
H.H. Nam, B.J. Kang, and K.H. Park. “Comparison of Computer and Human Face Recognition According to Facial Components,” Journal of Korea Multimedia Society, Vol. 37, No. 21, pp. 40-50, 2012.
A.R. Syafeeza, M. Halil-Hani, S.S. Liew, and R. Bakhteri, “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation,” International Journal of Engineering & Technology, Vol. 6, No. 1, pp. 44-57, 2014.
A. Toshev and C. Szegedy, “Deeppose: Human Pose Estimation via Deep Neural Networks,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, pp. 1653-1660, 2014.
Ｒ Chellappa, C L Wilson, and S Sirohey, “Human and machine recognition offaces: A survey,” Proceedings of the IEEE, Vol. 83, No. 5, pp. 705-740, 1995.
H.H. Nam, B.J. Kang, and K.H. Park. “Comparison of Computer and Human Face Recognition According to Facial Components,” Journal of Korea Multimedia Society, Vol. 37, No. 21, pp. 40-50, 2012.
A.R. Syafeeza, M. Halil-Hani, S.S. Liew, and R. Bakhteri, “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation,” International Journal of Engineering & Technology, Vol. 6, No. 1, pp. 44-57, 2014.
A. Toshev and C. Szegedy, “Deeppose: Human Pose Estimation via Deep Neural Networks,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, pp. 1653-1660, 2014.
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