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Reseach Article

Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA

by Shail Kumari Shah, Vineet Khanna
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 4
Year of Publication: 2015
Authors: Shail Kumari Shah, Vineet Khanna
10.5120/19818-1642

Shail Kumari Shah, Vineet Khanna . Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA. International Journal of Computer Applications. 113, 4 ( March 2015), 42-46. DOI=10.5120/19818-1642

@article{ 10.5120/19818-1642,
author = { Shail Kumari Shah, Vineet Khanna },
title = { Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 4 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number4/19818-1642/ },
doi = { 10.5120/19818-1642 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:38.865780+05:30
%A Shail Kumari Shah
%A Vineet Khanna
%T Facial Expression Recognition for Color Images using Gabor, Log Gabor Filters and PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 4
%P 42-46
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Facial expression recognition is an interesting and challenging problem, and found in many applications like human-computer interaction (HCI), robotics, video surveillance, border security, clinical research, person verification, crime prevention etc. . Facial expression is the movement of the muscles beneath the skin of the face. Through facial expressions human can convey their emotions without any verbal means. In this paper we have created raw database of color images. Training and testing set of images are created. Color information in an image is used to detect the face from the image. Important features from the detected face are extracted to form feature vectors using Gabor and Log Gabor filters. Principal Component Analysis (PCA) is used to reduce the dimension of the extracted features. Then these reduced features are classified using Euclidean distance. The main aim is to work upon three emotions-happy, neutral, surprise. Experiment carried out on self-generated database show comparable performance between Gabor and Log Gabor filters, where Log Gabor filters outperforming Gabor filters with classification accuracy of 86. 7%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gabor filters Log Gabor filters PCA Euclidean Distance YCbCr morphological FER.