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Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition

Aarati Venugopal Kartha, Vinayak Ashok Bharadi Published in Image Processing

IJAIS Proceedings on International Conference and Workshop on Communication, Computing and Virtualization
Year of Publication: 2015
© 2015 by IJAIS Journal
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  1. Aarati Venugopal Kartha and Vinayak Ashok Bharadi. Article: Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition. IJAIS Proceedings on International Conference and Workshop on Communication, Computing and Virtualization ICWCCV 2015(1):29-34, September 2015. BibTeX

    @article{key:article,
    	author = "Aarati Venugopal Kartha and Vinayak Ashok Bharadi",
    	title = "Article: Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition",
    	journal = "IJAIS Proceedings on International Conference and Workshop on Communication, Computing and Virtualization",
    	year = 2015,
    	volume = "ICWCCV 2015",
    	number = 1,
    	pages = "29-34",
    	month = "September",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Biometric authentication systems have become ubiquitous with the increasing number of surveillance cameras that are deployed almost everywhere, the use of biometric attendance systems and also its large scale use in forensic laboratories. Hyperspectral images are used widely in biometric research because of the immense amount of unique data they generate has proved to be helpful in solving the drawbacks of existing biometric systems. The main focus of the research was to use hyperspectral face images having 33 bands for face recognition using Fast Walsh transform coefficients. Face is a biometric trait which requires low user co-operation and provides better accuracy which makes it preferable over other biometric traits. With the use of hyperspectral face images, the accuracy rate was found to be improved. However the main drawback of these Hyperspectral images was that they generated large amount of redundant data and hence row, column and diagonal mean were computed instead of using the entire image so as reduce the memory and storage constraints. Orthogonal transforms such as Fast Walsh transform was used for texture feature extraction to generate the coefficients for the row, column and diagonal mean vectors. The extracted feature vectors are then subjected to intra class and inter class testing using Euclidian distance measure. The performance of the system was analysed.

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Keywords

Biometrics, Hyperspectral Images, Face Recognition, Fast Walsh Transform (FWHT).