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Number 9

Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors

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  • Priya Sisodia and Akilesh Verma and Sachin Kansal 2013. Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors. International Journal of Applied Information Systems. 5, 9 (July 2013), 9-13. DOI=http://dx.doi.org/10.5120/ijais450971 
  • @article{10.5120/ijais2017451568,
author = {Priya Sisodia and Akilesh Verma and Sachin Kansal},
title = {Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors},
journal = {International Journal of Applied Information Systems},
issue_date = {July 2013},
volume = {5},
number = {},
month = {July},
year = {2013},
issn = {},
pages = {9-13},
numpages = {},
url = {/archives/volume5/number9/508-0971},
doi = { 10.5120/ijais13-450971},
publisher = { xA9 2012 by IJAIS Journal},
address = {}
}

  • %1 450971
%A Priya Sisodia
%A Akilesh Verma
%A Sachin Kansal
%T Human Facial Expression Recognition using Gabor Filter Bank with Minimum Number of Feature Vectors
%J International Journal of Applied Information Systems
%@ 
%V 5
%N 
%P 9-13
%D 2013
%I  xA9 2012 by IJAIS Journal

Abstract

The Human Facial Expression Recognition is used in many fields such as mood detection and Human Computer Interaction (HCI). Gabor Filters are used to extract features. Gabor has the useful property of robustness against slight object rotation, distortion and variation in illumination. In the present work the effort has been made to provide the modules of for Human facial expression recognition by reducing the number of parameters use to represent Gabor feature the space complexity can reduce. SVM classifier has multi-classes. SVM classifies the expression by comparing it with the trained data.

References

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Keywords

Image Acquisition, Preprocessing, Feature Extraction, Classification

Index Terms

Computer Science
Information Sciences