Earticle

초록

영어

In this paper, feature extraction and facial recognition are studied in order to resolve problems like high-dimension problem, small size samples and no-linear separable problem that exist in facial recognition technology. In the part of feature extraction we use a Discrete Cosine Transform (DCT) algorithm, to extract the input features in building a face recognition system. The RBF neural network, which represents brilliant performance on small training sets, non-linear separable and high-dimension pattern recognition problems in the recognition stage, is used for pattern classification. The proposed approach is validated with the ORL database. Experimental results demonstrate the effectiveness of this method in the performance of face recognition.

목차

Abstract
 1. Introduction
 2. Feature Extraction in DCT Domain
 3. RBF Neural Network Algorithm for Face Recognition
  3.1. RBF Neural Network Structure
  3.2. Radical Basis Function
  3.3. RBF Neural Network Designing
  3.4. RBF Neural Network Hybrid Learning Algorithm
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Ning Zhang [ Department of Information & Communications Engineering, Tongmyong University, Busan, Korea ]
• Eung-Joo Lee [ Department of Information & Communications Engineering, Tongmyong University, Busan, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605