Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition
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ORIGINAL ARTICLE
Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition Saeed Najafi Khanbebin1 • Vahid Mehrdad1 Received: 1 June 2020 / Accepted: 4 November 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Face recognition applications focus on local features to prevent detailed information from being omitted while the feature extraction processes. This paper is based on presenting a local pattern-based model to extract more discriminative features that lead to more accurate classification. In local pattern-based feature extraction, the LBP is one of the most important approaches that many variants of this method have been proposed till now. LBP calculation is based on differences between the central pixel and the desired one. In contrast, the information hidden in the selected pixel’s neighborhood pixels is not included in this process. This paper proposes the DR_LBP approach to address this failure by defining distances and using some of them in a ratio form. Successful results have been earned in many experimental results. In LBP, the calculations’ primary flow takes advantage of two pixels in the LBP box, the central and the desired pixel. Contrary to the original LBP, this paper’s proposed approach uses three pixels of LBP box to conduct the feature vector, which leads to employing the information hidden in the relationship between neighboring pixels. This approach applies the experiments on two standard datasets, ORL Yale face and Faces94 dataset. The accuracy percent of the proposed plan is 95.95, 94.09 and 98.01 on ORL, Yale face and Faces94 dataset, respectively, which is the reason to present this model as a new face feature extraction approach. Keywords Distance ratio local binary patterns DR_LBP LBP Face recognition Feature extraction
1 Introduction A digital image is more than just a bunch of numbers in rows and columns gathered into a matrix and saved in a cell, etc. Images carry information in themselves. Those rows and columns are full of useful information. The computer vision task is a way of extracting the information included in images and making it useable for computers, to make the scenes easy to understand. The feature years would be the age of a change in society that is applied by computer vision. In industrial processes by controlling the quality of products, in medical & Vahid Mehrdad [email protected] Saeed Najafi Khanbebin [email protected] 1
Department of Electrical and Electronics Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran
applications by diagnosis disease, in satellite images, identifying people, etc. are the contexts that computer vision plays a critical role in their progressing abilities [1]. Face recognition has been one of the frameworks that attracted many researchers. After the first attempts in the 1970s, when an impressive increase in computational power was happening in1988, the first boom has occurred
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