Title: Face recognition using local binary pattern and Gabor-Kernel Fisher analysis

Authors: Tulasi Krishna Sajja; Hemantha Kumar Kalluri

Addresses: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ' Department of Computer Science and Engineering, SRM University, Guntur District, 522240, AP, India

Abstract: Face recognition technology is one of the everyday tasks in our daily life. But, recognising the correct face with high accuracy from large databases is a challenging task. To overcome this challenge, feature fusion of local binary pattern (LBP) with Gabor-Kernel Fisher analysis (Gabor-KFA) has proposed for face recognition. In this method, by using Gabor filter, extract Gabor features from a face image, on the other hand, extract features from LBP coded face image, then combined these extracted features generate high dimensional feature space. With this high dimensionality features, the complexity of training time and identification time may increase. To avoid this complexity, the Kernel Fisher analysis algorithm was adopted to reduce the feature vector size. Experiments were conducted separately on Gabor features and also on fused features. To test the performance of the proposed approach, the experiments were performed on the IIT Delhi database, ORL database, and FR database.

Keywords: face recognition; biometrics; Gabor filter; KFA; Kernel Fisher analysis; LBP; local binary pattern; feature fusion.

DOI: 10.1504/IJAIP.2023.133254

International Journal of Advanced Intelligence Paradigms, 2023 Vol.26 No.1, pp.28 - 42

Received: 29 Mar 2019
Accepted: 08 Sep 2019
Published online: 04 Sep 2023 *

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