Weighted Modular Image Principal Component Analysis for face recognition

•We propose two feature extraction methods for face recognition: MIMPCA and wMIMPCA.•The proposed methods use modular PCA to minimize local variation.•The proposed methods deal with changes in illumination and head pose.•The proposed methods obtained better results compared with other methods. This...

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Bibliographic Details
Published in:Expert systems with applications 2013-09, Vol.40 (12), p.4971-4977
Main Authors: Cavalcanti, George D.C., Ren, Tsang Ing, Pereira, José Francisco
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Data processing. List processing. Character string processing
Distortion
Exact sciences and technology
Expert systems
Face recognition
Image feature extraction
Memory organisation. Data processing
Modular
Modular PCA
Pattern recognition. Digital image processing. Computational geometry
Principal Component Analysis
Software
Strategy
Weight reduction
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Summary:•We propose two feature extraction methods for face recognition: MIMPCA and wMIMPCA.•The proposed methods use modular PCA to minimize local variation.•The proposed methods deal with changes in illumination and head pose.•The proposed methods obtained better results compared with other methods. This paper proposes two feature extraction techniques that minimizes the effects of distortions generated by variations in illumination, rotation and, head pose in automatic face recognition systems. The proposed techniques are Modular IMage Principal Component Analysis (MIMPCA) and weighted Modular Image Principal Component Analysis (wMIMPCA). Both techniques are based on PCA and they use the modular image decomposition to minimize local variation. Also, the covariance matrix is calculated directly from the original image matrix. This strategy generates a smaller matrix compared with traditional PCA and reduces the computational effort. wMIMPCA assumes that parts of the face are more discriminatory than others, so a Genetic Algorithm is used to obtain weights for each region in the face image. The proposed techniques are compared with Modular PCA and two-dimensional PCA using three well-known databases, showing better results.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.03.003