Face recognition with illumination, scale and rotation invariance using multiblock LTP-GLCM descriptor and adaptive ANN

Sachinkumar Veerashetty (), Virupakshappa () and Ambika ()
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Sachinkumar Veerashetty: Sharnbasva University
Virupakshappa: Sharnbasva University
Ambika: Sharnbasva University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 16, 174-187

Abstract: Abstract Face recognition has recently gained significant attention as one of the most useful image analysis applications. By leveraging their unique but incredible identification skills, these systems are capable of recognizing users. Face recognition systems have been extensively studied. The system, however, has a number of drawbacks. Existing face recognition methods may result in a longer histogram, which slows down for a large-scale database. To address the challenges with face recognition, we have proposed a hybrid descriptor using MultiBlock Local Ternary Pattern (LTP)—Gray Level Co- occurrence Matrix (GLCM). In this study, we have employed the LTP, GLCM and Speeded Up Robust Features (SURF) methods to extract the illumination, rotation, and scale-invariant features of the face database images. These features are then trained using Artificial Neural Network. The layer neurons are optimally selected by Crow Search Optimization (CSO) method which yielded an accuracy of 95%. The proposed approach was implemented in the Matlab software, and the experimental data was analyzed to show that the developed texture descriptor has a higher recognition rate than existing methods.

Keywords: Face recognition; Local ternary pattern; Gray level co- occurrence matrix; Speeded up robust features; Artificial neural network; Crow search optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01688-0

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