A novel statistical feature analysis-based global and local method for face recognition

Ahmed Talab, Mohammed and Suryanti, Awang and Ansari, Mohd Dilshad (2020) A novel statistical feature analysis-based global and local method for face recognition. International Journal of Optics, 2020 (4967034). pp. 1-17. ISSN 1687-9384. (Published)

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Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Binary images; Decision trees; Image analysis; Image enhancement; Matrix algebra; Optical character recognition; Textures
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 30 Jun 2021 14:26
Last Modified: 30 Jun 2021 14:26
URI: http://umpir.ump.edu.my/id/eprint/30495
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