In this paper, a combination methodology of Discrete Cosine Transform (DCT) and an improved D-LDA and Neural Networks was proposed. DCT can compress the information of original signal efficiently, so we reduce the dimension firstly and then extract features by improved D-LDA on the low dimension space to overcome the shortages of LDA maximally. After calculating the eigenvectors and a new Fisher’s criterion using improved D-LDA algorithm we proposed, the projection vectors are calculated for the training set and then used to train the neural networks for human identity. The experimental results on ORL face database show that this combined method has well performance.
목차
Abstract 1. Introduction 2. Feature Extraction in DCT Domain 3. Improved D-LDA Algorithm for Feature Extraction 4. Integrated BPNNs Algorithm for Face Recognition 5. Experimental Results 6. Conclusion Acknowledgements References
키워드
Human face recognitionDCTimproved D-LDABPNNs
저자
Wenkai Xu [ Department of Information & Communications Engineering, Tongmyong University, Busan, Korea ]
Eung-Joo Lee [ Department of Information & Communications Engineering, Tongmyong University, Busan, Korea ]
보안공학연구지원센터(IJSIA) [Science & Engineering Research Support Center, Republic of Korea(IJSIA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Security and Its Applications
간기
격월간
pISSN
1738-9976
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Security and Its Applications Vol.6 No.2