Advanced facial recognition with LBP-URIGL hybrid descriptors

Hendi, Sajjad H. and Taher, Hazeem B. and Hussein, Karim Q. (2024) Advanced facial recognition with LBP-URIGL hybrid descriptors. POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES, 19 (3). pp. 14-21. ISSN 1788-1994

Text 606-article-p14.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Facial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.

Actions (login required)

Edit Item