Research Article
Feature Image Generation Using Energy Distribution for Face Recognition in Transform Domain
@INPROCEEDINGS{10.1007/978-3-642-27308-7_68, author={Vikas Maheshkar and Sushila Kamble and Suneeta Agarwal and Vinay Srivastava}, title={Feature Image Generation Using Energy Distribution for Face Recognition in Transform Domain}, proceedings={Advances in Computer Science and Information Technology. Computer Science and Engineering. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part II}, proceedings_a={CCSIT PATR II}, year={2012}, month={11}, keywords={Face Recognition Discrete Cosine Transform (DCT) Slant Transform (ST) Walsh Transform (WT) Energy Distribution (ED) Mahalanobis distance}, doi={10.1007/978-3-642-27308-7_68} }- Vikas Maheshkar
Sushila Kamble
Suneeta Agarwal
Vinay Srivastava
Year: 2012
Feature Image Generation Using Energy Distribution for Face Recognition in Transform Domain
CCSIT PATR II
Springer
DOI: 10.1007/978-3-642-27308-7_68
Abstract
In this paper, we propose a feature image generation method for face recognition. Feature extraction is done using three transforms viz. Discrete Cosine Transform, Slant Transform and Walsh Transform. Energy distribution defined as magnitude of effective information is used to create a feature image in transform domain by retaining high energy distribution coefficients. The proposed method consists of three steps. First, the face images are transformed into the frequency domain. Second, transformed coefficient matrix and energy distribution matrix is divided into three equal regions. Thresholds are selected in each region to retain the most significant features. Finally feature image is generated from these coefficients. Recognition is performed on generated feature images using Mahalanobis distance. Experimental results shows that the proposed method improve the face recognition rate as compared to previously proposed methods.