SVM classifier for face recognition based on unconstrained correlation filter

Author

Banerjee, Pradipta K. ; Chandra, Jayanta K. ; Datta, Asit K.

Author_Institution

Dept. of Electr. Eng., Future Inst. of Eng. & Manage., Kolkata, India

fYear

2009

fDate

18-19 Nov. 2009

Firstpage

290

Lastpage

294

Abstract

In this paper we present a novel method of face recognition technique using a combination of unconstrained correlation filter and support vector machine. The unconstrained minimum average correlation energy (UMACE) filter generates a recognition parameter based on peak to side lobe ratio (PSR). Instead of training the support vector machine by the face image for classification, the PSR values from a set of UMACE filters is used to train the SVM. The proposed technique is tested with Cropped Yale B illumination database and the method shows significant reduction in error rate compared to classical UMACE filter based technique.

Keywords

correlation methods; face recognition; filtering theory; learning (artificial intelligence); support vector machines; Cropped Yale B illumination database; SVM classifier; UMACE filter; face recognition; peak-to-side lobe ratio; support vector machines; unconstrained correlation filter; unconstrained minimum average correlation energy; Face recognition; Fourier transforms; Frequency domain analysis; Lighting; Linear discriminant analysis; Optical filters; Signal processing; Support vector machine classification; Support vector machines; Testing; Face Recognition; Illumination; PSR; SVM; Unconstrained Correlation filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Image Processing Applications (ICSIPA), 2009 IEEE International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-5560-7

Type

conf

DOI

10.1109/ICSIPA.2009.5478663

Filename

5478663