Face Recognition Using Class Specific Space Model

In this paper, we investigate the face recognition problem via clustering of frontal face images represented in frequency domain by low frequency discrete cosine transform (DCT) coefficients. Our approach termed as class specific space model (CSSM) is based on the assumption that faces of different...

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Bibliographic Details
Published in2009 International Conference on Advances in Recent Technologies in Communication and Computing pp. 160 - 164
Main Authors Bhat, G., Achary, K.K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Dimensionality reduction
Discrete cosine transforms
Face recognition
feature space
Gaussian mixture model s
Image databases
Image reconstruction
Image storage
Linear discriminant analysis
parsimonious models
Principal component analysis
Space technology
Spatial databases
Testing
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Summary:In this paper, we investigate the face recognition problem via clustering of frontal face images represented in frequency domain by low frequency discrete cosine transform (DCT) coefficients. Our approach termed as class specific space model (CSSM) is based on the assumption that faces of different subjects are clustered in different low dimensional subspace of the feature space. The proposed approach uses 2D-DCT for feature extraction, each of the class clusters in the feature space are later modeled under Gaussian mixture model framework by a set of parameters which best fit the data. The proposed approach is tested on AR face database and its effectiveness in terms of identification rate is compared with the conventional IPCA and DLDA-SVM based classifiers.
ISBN:9781424451043
1424451043
DOI:10.1109/ARTCom.2009.234