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

Author

Listed:
  • Sachinkumar Veerashetty

    (Sharnbasva University)

  • Virupakshappa

    (Sharnbasva University)

  • Ambika

    (Sharnbasva University)

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.

Suggested Citation

  • Sachinkumar Veerashetty & Virupakshappa & Ambika, 2024. "Face recognition with illumination, scale and rotation invariance using multiblock LTP-GLCM descriptor and adaptive ANN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 174-187, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01688-0
    DOI: 10.1007/s13198-022-01688-0
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