This paper presents the comparison between
the methodologies used for human emotion recognition
from face images based on textural analysis and KNN
classifier. Automatic facial expression recognition (FER)
plays an important role in Human Computer Interaction
(HCI) systems for measuring people’s emotions has
dominated psychology by linking expressions to a group of
basic emotions (i.e., anger, disgust, fear, happiness,
sadness, and surprise).The comparative study of Facial
Expression Recognition involves Curvelet transform based
Robust Local Binary Pattern (RLBP) and Distinct LBP
(DLBP) features and features derived from DLBP and
GLCM. The objective of this research is to show that
features derived from RLBP with DLBP is superior to the
features derived from DLBP and GLCM. To test and
evaluate their performance, experiments are performed
using Japanese Female Expressions Model (JAFEE)
database in both techniques. The comparison chart shows
that, the DLBP and RLBP based feature extraction with
KNN classifier gives much better accuracy than other
existing method.
Published In:IJCSN Journal Volume 5, Issue 6
Date of Publication : December 2016
Pages : 941-947
Figures :11
Tables : --
Prashant P Thakare : Department of Communication Engineering, S.S.V.P.S B.S.D. College of Engineering Dhule,
North Maharashtra University, Maharashtra, India.
Pravin S Patil : Department of Electronics and Communication Engineering, S.S.V.P.S B.S.D. College of Engineering Dhule,
North Maharashtra University, Maharashtra, India.
In this paper, we compare the proposed method with the
existing method. A proposed technique for facial
expression recognition is the combination of curvelet
transform, DLBP and RLBP. The features are extracted
from still images. Thus the proposed integrated method
represents complete information of the facial image. The
proposed DLBP & RLBP of FCI is a three phase model
for recognizing facial expressions. In the first Phase it,
reduced the 5x 5 image in to a 3x 3 sub image without
losing any significant information. In the second and
third phases Curvelet transform, Distinct LBP. Then
apply RLBP at last we had used KNN classifier to find
the expression of input image. The proposed method
overcomes the unpredictable distribution of the face
images in real environment caused by statistical methods
and illumination problems caused by LBP. The existing
method extracts the features derived from distinct LBP
and GLCM. The comparison chart shows that, the DLBP
and RLBP based feature extraction with knn classifier
gives much better accuracy with lesser algorithmic
complexity than other facial expression recognition
approaches.
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