Elsevier

Procedia Computer Science

Volume 46, 2015, Pages 1458-1467
Procedia Computer Science

Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique

https://doi.org/10.1016/j.procs.2015.02.065Get rights and content
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Abstract

Pose and illumination variation in Face Recognition (FR) is a problem of fundamental importance in computer vision. We propose to tackle this problem by using Chirp Z-Transform (CZT) and Goertzel algorithm as preprocessing, Block-based feature extraction and Exponential Binary Particle Swarm Optimization (EBPSO) for feature selection. Every stage of the FR system is examined and an attempt is made to improve each stage. The unique combination of CZT and Goertzel algorithm is used for illumination normalization. The proposed feature extractor uses a unique technique of Block based Additive Fusion of the image. EBPSO is a feature selection algorithm used to select the optimal feature subset. The proposed approach has been tested on four benchmark face databases, viz., Color FERET, HP, Extended Yale B and CMU PIE datasets, and demonstrates better performance compared to existing methods in the presence of pose and illumination variations.

Keywords

Face recognition
Feature extraction
Image preprocessing
Feature selection
Particle Swarm Optimization

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Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).