지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.12 No.3
- 발행연도
- 2012.9
- 수록면
- 250 - 255 (6page)
이용수
초록· 키워드
오류제보하기
Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.
목차
Abstract
1. Introduction
2. Photon Counting Linear Discriminant Analysis
3. Experimental and Simulation Results
4. Conclusion
References
참고문헌 (14)
참고문헌 신청
1. [학술지(정기간행물)] - W. Zhao / 2003 / Face recognition: A literature survey / ACM Computing Surveys 35 (4) : 399 ~ 458
2. [학술지(정기간행물)] - Rabia Jafri / 2009 / A Survey of Face Recognition Techniques / JIPS(Journal of Information Processing Systems) 5 (2) : 41 ~ 68
3. [학술지(정기간행물)] - P. N. Belhumer / 1997 / Eigenfaces vs. Fisherfaces: recognition using class specific linear projection / IEEE Trans. Pattern Analysis and Machine Intelligence 19 (7) : 711 ~ 720
4. [학술지(정기간행물)] - D. L. Swets / 1996 / Using discriminant eigenfeatures for image retrieval / IEEE Trans. Pattern Analysis and Machine Intelligence 18 : 831 ~ 836
5. [학술지(정기간행물)] - J. H. Friedman / 1989 / Regularized discriminant analysis / J. Am. Statistical Assoc 84 (405) : 165 ~ 175
2. [학술지(정기간행물)] - Rabia Jafri / 2009 / A Survey of Face Recognition Techniques / JIPS(Journal of Information Processing Systems) 5 (2) : 41 ~ 68
3. [학술지(정기간행물)] - P. N. Belhumer / 1997 / Eigenfaces vs. Fisherfaces: recognition using class specific linear projection / IEEE Trans. Pattern Analysis and Machine Intelligence 19 (7) : 711 ~ 720
4. [학술지(정기간행물)] - D. L. Swets / 1996 / Using discriminant eigenfeatures for image retrieval / IEEE Trans. Pattern Analysis and Machine Intelligence 18 : 831 ~ 836
5. [학술지(정기간행물)] - J. H. Friedman / 1989 / Regularized discriminant analysis / J. Am. Statistical Assoc 84 (405) : 165 ~ 175
이 논문의 저자 정보
이 논문과 함께 이용한 논문
PCA기반 CNN을 이용한 사용자 행위 인식
김여리
,
소현우
,
이영민
외 1명
한국정보과학회 학술발표논문집
2018
.12
경찰청 112 신고 데이터의 미분류 유형에 대한 LDA 토픽모델링 기반 잠재적 신고유형 분석
김용진
,
유덕산
,
방준성
외 1명
한국통신학회 학술대회논문집
2020
.08
LDA 모델을 이용한 학술지 자원환경지질의 연구 동향 분석
허준용
,
김태용
,
양민준
대한지질학회 학술대회
2020
.10
LDA 기법을 이용한 미세먼지 이슈의 토픽모델링 분석
윤순욱
,
김민철
에너지공학
2020
.06
LDA와 선호도 전파 알고리즘을 이용한 아동 발화의 화제 수 측정
오세은
,
허탁성
,
이윤경
외 1명
한국정보과학회 학술발표논문집
2019
.12
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2014-028-001358059