Expressions Recognition of North-East Indian (NEI) Faces
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Facial expression is one of the major distracting factors for face recognition performance. Pose and illumination variations on face images also influence the performance of face recognition systems. The combination of three variations (facial expression, pose and illumination) seriously degrades the recognition accuracy. In this paper, three experimental protocols are designed in such a way that the successive performance degradation due to the increasing variations (expressions, expressions with illumination effect and expressions with illumination and pose effect) on face images can be examined. The whole experiment is carried out using North-East Indian (NEI) face images with the help of four well-known classification algorithms namely Linear Discriminant Analysis (LDA), K-Nearest Neighbor algorithm (KNN), combination of Principal Component Analysis and Linear Discriminant Analysis (PCA + LDA), combination of Principal Component Analysis and K-Nearest Neighbor algorithm (PCA + KNN). The experimental observations are analyzed through confusion matrices and graphs. This paper also describes the creation of NEI facial expression database, which contains visual static face images of different ethnic groups of the North-East states. The database is useful for future researchers in the area of forensic science, medical applications, affective computing, intelligent environments, lie detection, psychiatry, anthropology, etc.
KeywordsVisual face image Facial expressions Pose and illumination variations NEI facial expression database Baseline algorithms
The work presented here is being conducted in the Biometrics Laboratory of Tripura University, under the research project supported by the Grant No. 12(2)/2011-ESD, dated 29/03/2011, from DeitY, MCIT, Government of India. The first author is grateful to Department of Science and Technology (DST), Government of India for providing her Junior Research Fellowship-Professional (JRF-Professional) under DST INSPIRE fellowship program (No. IF131067). The authors would like to thank anonymous reviewers for their comments/suggestions to improve the quality of the paper.
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