CFP last date
15 March 2024
Reseach Article

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

Published on September 2015 by Aarati Venugopal Kartha, Vinayak Ashok Bharadi
International Conference and Workshop on Communication, Computing and Virtualization
Foundation of Computer Science USA
ICWCCV2015 - Number 1
September 2015
Authors: Aarati Venugopal Kartha, Vinayak Ashok Bharadi
ac131ab1-6d60-4c86-bb7b-6cecc507ea0a

Aarati Venugopal Kartha, Vinayak Ashok Bharadi . Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition. International Conference and Workshop on Communication, Computing and Virtualization. ICWCCV2015, 1 (September 2015), 0-0.

@article{
author = { Aarati Venugopal Kartha, Vinayak Ashok Bharadi },
title = { Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition },
journal = { International Conference and Workshop on Communication, Computing and Virtualization },
issue_date = { September 2015 },
volume = { ICWCCV2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwccv2015/number1/790-1559/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Communication, Computing and Virtualization
%A Aarati Venugopal Kartha
%A Vinayak Ashok Bharadi
%T Performance Analysis of Feature Vector based on Walsh Transform Coefficients of Row, Column and Diagonal Means for Hyperspectral Face Recognition
%J International Conference and Workshop on Communication, Computing and Virtualization
%@ 2249-0868
%V ICWCCV2015
%N 1
%P 0-0
%D 2015
%I International Journal of Applied Information Systems
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.

References
  1. V A Bharadi and Payal Mishra, "Multidimensional Clustering based Feature Vector Extraction for Hyperspectral Face Recognition", IJAIS, Feb 2012.
  2. V A Bharadi, Bhavesh Pandya and Bhushan Nemade, "Multimodal Biometric Recognition using Iris & Fingerprint By Texture Feature Extraction using Hybrid Wavelets", International Conference & Workshop on Emerging Trends in Technology, 2011.
  3. V A Bharadi, Payal Mishra and Bhavesh Pandya , "Multimodal Face Recognition Using Multidimensional Clustering on Hyperspectral Face Images", International Conference & Workshop on Emerging Trends in Technology, 2011.
  4. Zhihong Pan, Glenn Healey, Manish Prasad, and Bruce Tromberg, "Face Recognition in Hyperspectral Images, IEEE Transactions On Pattern Analysis And Machine Intelligence", Vol. 25, No. 12, December 2003
  5. Xudong Kang, Shutao Li, Leyuan Fang and Jón Atli Benediktsson, "Intrinsic Image Decomposition For Feature extraction Of Hyperspectral Images", IEEE Transactions On Geoscience And Remote Sensing, Vol. 53, No. 4, April 2014
  6. Wei Di, Lei Zhang, David Zhang and Quan Pan," Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection", IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 40, No. 6, November 2010.
  7. Anil K. Jain and Umut Uludag, "Hiding Biometric Data", IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 25, No. 11, November 2003.
  8. H B Kekre and V A Bharadi, "Biometric Authentication Systems", PhD. Thesis Submitted to NMIMS University, Dec 2011.
  9. H B Kekre and Kamal Shah, "Face Recognition Using Orthogonal Transforms and Vector Quantization Techniques, PhD. Thesis Submitted to NMIMS University, 2010.
  10. Hector Erives and Nicholas B. Targhetta, "Implementation Of A 3-D Hyperspectral Instrument For Skin Imaging Application", IEEE Transactions On Instrumentation And Measurement, Vol. 58, No. 3, March 2009.
  11. Amit Mukherjee, Miguel Velez-Reyes and Badrinath Roysam," Interest Points For Hyperspectral Image Data" ,IEEE Transactions On Geoscience And Remote Sensing, Vol. 47, No. 3, March 2009.
  12. Emmanuel Christophe, Dominique Léger, and Corinne Mailhes, "Quality Criteria Benchmark For Hyperspectral Imagery, IEEE Transactions On Geoscience And Remote Sensing, Vol. 43, No. 9, September 2005.
  13. H B Kekre , VA Bharadi , P P Janrao and V I Singh, "Face Recognition using Kekre's Wavelets Energy & Performance Analysis of Feature Vector Variants", International Conference & Workshop on Emerging Trends in Technology, 2011.
  14. H B Kekre, V A Bharadi, S Tauro and V I Singh , "Performance Comparison of DCT, FFT, WHT & Kekre's Transform for On-Line Signature Recognition", International Conference & Workshop on Emerging Trends in Technology , 2011.
  15. T K Sarode and Prachi Patil, "Comparing Transform Domain Techniques and Vector Quantization Techniques for Face Detection and Recognition in Digital Images", International Journal of Computer Application, Volume 49– No. 4, July 2012.
  16. V. A Bharadi and Pallavi Vartak, "Hyperspectral Face Recognition by Texture Feature Extraction using Hybrid Wavelets Type I and Type II and Kekre's Wavelet Transform,ICCUBEA,PCCOE,Pune,2015.
  17. V. A Bharadi and Pallavi Vartak," Performance Improvement of Hyperspectral Face Recognition by Multimodal and Multi-Algorithmic Feature Fusion of Hybrid and Kekre Wavelet based Feature Vector, ICCUBEA, PCCOE, Pune, 2015.
  18. PolyU Hyperspectral Face Database [Online] Available:http://www4. comp. polyu. edu. hk/~biometrics/hsi/hyper_face. htm.
Index Terms

Computer Science
Information Sciences

Keywords

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