AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM

Authors

  • Sasi Kumar Balasundaram The Rajaas Engineering College
  • J. Umadevi The Rajaas Engineering College
  • B. Sankara Gomathi National Engineering College

DOI:

https://doi.org/10.24297/ijct.v8i2.3386

Keywords:

Learning, color space recognition, color-component, weighted feature fusion, color feature selection, nearest feature space and color space conversion

Abstract

This paper aims to achieve the best color face recognition performance. The newly introduced feature selection method takes advantage of novel learning which is used to find the optimal set of color-component features for the purpose of achieving the best face recognition result. The proposed color face recognition method consists of two parts namely color-component feature selection with boosting and color face recognition solution using selected color component features. This method is better than existing color face recognition methods with illumination, pose variation and low resolution face images. This system is based on the selection of the best color component features from various color models using the novel boosting learning framework. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. The effectiveness of color face recognition method has been successfully evaluated by the public face databases.

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Author Biographies

Sasi Kumar Balasundaram, The Rajaas Engineering College

Professor, Department of Computer  Science

J. Umadevi, The Rajaas Engineering College

Department of Computer  Science

B. Sankara Gomathi, National Engineering College

Dean (Academic)

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Published

2013-06-20

How to Cite

Balasundaram, S. K., Umadevi, J., & Gomathi, B. S. (2013). AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 8(2), 787–795. https://doi.org/10.24297/ijct.v8i2.3386

Issue

Section

Research Articles