LBP based on multi wavelet sub-bands feature extraction used for face recognition

Rashid, Rasber D. and Jassim, Sabah A. and Sellahewa, Harin (2013) LBP based on multi wavelet sub-bands feature extraction used for face recognition. In: 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013, 22-25 Sep 2013, Southampton.

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Official URL: http://doi.org/10.1109/MLSP.2013.6661911

Abstract

The strategy of extracting discriminant features from a face image is immensely important to accurate face recognition. This paper proposes a feature extraction algorithm based on wavelets and local binary patterns (LBPs). The proposed method decomposes a face image into multiple sub-bands of frequencies using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping sub-regions. Then LBP histograms based on the traditional 8-neighbour sampling points are extracted from the approximation sub-band, whilst 4-neighbour sampling points are used to extract LBPHs from detail sub-bands. Finally, all LBPHs are concatenated into a single feature histogram to effectively represent the face image. Euclidean distance is used to measure the similarity of different feature histograms and the final recognition is performed by the nearest-neighbour classifier. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combination of sub-bands. Results show that the proposed method outperforms the traditional LBP based features.

Item Type: Conference or Workshop Item (Poster)
Additional Information: © 2013 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Biometrics, face recognition, discrete wavelet transform, local binary pattern, feature extraction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Science > Applied Computing
Depositing User: Harin Sellahewa
Date Deposited: 31 Aug 2015 13:13
Last Modified: 09 Jun 2016 10:17
URI: http://bear.buckingham.ac.uk/id/eprint/47

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