Earticle

초록

영어

The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. PCA and LDA are used for the feature extraction and the resultant feature vectors are fused with the different fusion techniques and the proposed method yields good recognition rate with PCA Fusion of PCA and LDA features and those are classified with neural network. In general the size of the face database is too high and it needs more memory and needs more time for training so that to improve time and space complexities there is a need for dimensionality reduction. The extracted features are classified with Neural Network to improve the recognition rate.

목차

Abstract
 1. Introduction
 2. Background Work
  2.1. Existing System
  2.2. Proposed System
  2.3. Training
 3. Testing and Comparison Study
  3.1. Training and Testing of Neural Networks
  3.2. System Performance
  3.3. Comparison with previous FR methods
  3.4. Total Error Rate
  3.5. Performance Across Databases
 4. Conclusions
 References

키워드

저자

• Venkata Naresh Mandhala [ Dept. of Information Technology, VFSTR University Vadlamudi, Guntur, India ]
• Debnath Bhattacharyya [ Dept. of Computer Science and Engineering, VFSTR University Vadlamudi, Guntur, India ]
• Tai-hoon Kim [ Dept. of Convergence Security, Sungshin Women's University, 249-1, Dongseon-dong 3-ga, Seoul, 136-742, Korea ] Corresponding Author

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