Earticle

초록

영어

Face recognition is the process of identifying one or more people in images or videos. It is an important part of biometric, security & surveillance system, and image indexing systems. Various face recognition techniques have been proposed in literature such as: Eigen-faces, Feature based, Hidden Markov model and Neural network based techniques. The first three techniques mostly include a phase of feature extraction or preprocessing closely related to the type of image to recognize. On the other hand Neural network technique does not need specific data about the type of image, thus can be applied to any type of image and at the same time provides better accuracy. In this paper we made an effort to combine neural network technique with fuzzy logic. Our experimental result shows that combining the two provide better accuracy in comparison to other techniques mentioned above.

목차

Abstract
 1. Introduction
 2. Face Recognition Techniques
  2.1. Eigenfaces
  2.2. Feature-based
  2.3. Hidden Markov Model
  2.4. Neural Techniques
 3. Proposal
  3.1. Training of the Neural Network:
  3.2. Recognition of the data using Fuzzy Logic:
 4. Simulation Set up Parameters
  4.1. Designed Architecture
  4.2. Performance Metrics
  4.3. Setup Parameters
 5. Simulation Results
  5.1. Impact on Accuracy
  5.2. Impact on Error Rate
 6. Conclusion
 References

키워드

저자

• Shweta Mehta [ YMCA University of Science and Technology ]
• Shailender Gupta [ YMCA University of Science and Technology ]
• Bharat Bhushan [ YMCA University of Science and Technology ]
• C. K. Nagpal [ Echleon Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605