Open Access

ARTICLE

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Khuram Nawaz Khayam¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Muhammad Usman Ashraf⁴, Usman Tariq⁵, Mohammed Nawaf Altouri⁶, Khalid Alsubhi⁷

1 Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
2 Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
3 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20122, Italy
4 Department of Computer Science, University of Management and Technology, Sialkot, Pakistan
5 College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, 16278, Saudi Arabia
6 Department of Computer Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, 13318, Saudi Arabia
7 Department of Computer Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Zahid Mehmood. Email:

Computers, Materials & Continua 2022, 70(3), 5039-5058. https://doi.org/10.32604/cmc.2022.019975

Received 04 May 2021; Accepted 03 August 2021; Issue published 11 October 2021

Abstract

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.

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Keywords

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