Paper Info
Title
Face Recognition Through Different Facial Expressions
Abstract
Face recognition has become an accessible issue for experts as well as ordinary people as it is a focal non-interfering biometric modality. In this paper, we introduced a new approach to perform face recognition under varying facial expressions. The proposed approach consists of two main steps: facial expression recognition and face recognition. They are two complementary steps to improve face recognition across facial expression variation. In the first step, we selected the most expressive regions responsible for facial expression appearance using the Mutual Information technique. Such a process helps not only improve the facial expression classification accuracy but also reduce the features vector size. In the second step, we used the Principal Component Analysis (PCA) to build EigenFaces for each facial expression class. Then, a face recognition is performed by projecting the face onto the corresponding facial expression Eigenfaces. The PCA technique significantly reduces the dimensionality of the original space since the face recognition is carried out in the reduced Eigenfaces space. An experimental study was conducted to evaluate the performance of the proposed approach in terms of face recognition accuracy and spatial-temporal complexity.
Year
DOI
Venue
2015
10.1007/s11265-014-0967-z
Signal Processing Systems
Keywords
Field
DocType
Facial expression recognition,Face recognition,Local Binary Pattern (LBP),Principal Component Analysis (PCA),Mutual information,Support Vector Machine (SVM)
Facial recognition system,Face hallucination,Eigenface,Pattern recognition,Three-dimensional face recognition,Computer science,Speech recognition,Facial expression,Artificial intelligence,Mutual information,Biometrics,Principal component analysis
Journal
Volume
Issue
ISSN
81
3
1939-8018
Citations 
PageRank 
References 
3
0.37
52
Authors
3
Name
Order
Citations
PageRank
Mliki Hazar1114.91
Emna Fendri2127.28
Mohamed Hammami318130.54