Earticle

초록

영어

Face recognition has been a very active research area in the past two decades. Many attempts have been made to understand the process how human beings recognize human faces. It is widely accepted that face recognition may depend on both componential information (such as eyes, mouth and nose) and non-componential/holistic information (the spatial relations between these features), though how these cues should be optimally integrated remains unclear. The present study, a different observer's view approach using eigen/fisher features of multi-scaled face components and Artificial Neural Network has been proposed. The basic idea of the proposed method is to construct facial feature vector by down sampling face components such as eyes, nose, mouth and whole face with different resolutions based on significance of face component, and then subspace Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) method is employed for further dimensionality reduction and good representation of facial features. Each face in data base to be recognized is projected on eigen space or fisher face to find its weight vector. The weight vector of face images to be trained become the input to neural network classifier, which uses Back Propagation/Radial basis function to recognize faces with variation in facial expression, and with / without spectacles. The proposed algorithm has been tested on 400 faces of 10 subjects of ORL data base and 500 faces of 100 subjects of FERET database results are encouraging compared to the existing methods in literature.

목차

Abstract
 1. Introduction
  1.1) Holistic Approach:
  1.2) Component Based Approach:
  1.3) Hybrid Approach:
 2. Pre-processing Phase
  2.1) Average Filtering
  2.2) Histogram Equalization
  2.3) Bi-Cubic Interpolation
 3. PCA & LDA Methods
  3.1) Principal Component Analysis
  3.2 Linear Discriminant Analysis
 4. ANN for Face Recognition
  4.1) Back Propagation:
  4.2) Radial Basis Function Network
 5. Proposed Algorithm for Face Recognition
  5.1) Flow Chart for Proposed Method:
  5.2) Implementation:
 6. Results and Discussion
  6.1) Experimental Setup:
  6.2) Simulation Result:
 7. Conclusion
 References

키워드

저자

• Prof. K.Rama Linga Reddy [ HOD, ETM GNITS, Hyderabad ]
• Prof G.R. Babu [ KMIT, Hyderabad ]
• Dr. Lal Kishore [ JNTU, Hyderabad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605