Multilinear principal component analysis for face recognition with fewer features

Authors:
Jin Wang;Armando Barreto;Lu Wang;Yu Chen;Naphtali Rishe;Jean Andrian;Malek Adjouadi
Affiliations:
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA;Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA;Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA;Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA;The School of Computing and Information Science, Florida International University, Miami, FL 33174, USA;Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA;Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Venue:
Neurocomputing
Year:
2010

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Abstract

In this study, a method is proposed based on multilinear principal component analysis (MPCA) for face recognition. This method utilized less features than traditional MPCA algorithm without downgrading the performance in recognition accuracy. The experiment results show that the proposed method is more suitable for large dataset, obtaining better computational efficiency. Moreover, when support vector machine is employed as the classification method, the superiority of the proposed algorithm reflects significantly.