Steerable pyramid transform and local binary pattern based robust face recognition for e-health secured login. (July 2016)

• Record Type: Journal Article
• Authors: Alelaiwi, Abdulhameed; Abdul, Wadood; Dewan, M. Solaiman; Migdadi, Mahmoud; Muhammad, Ghulam
• Is Part Of: Computers & electrical engineering. Volume 53(2016)
• Page Start: 435
• Page End: 443
• Publication Date: 2016-07
• Subjects: Steerable pyramid transform -- Local binary pattern -- e-Health -- Face recognition; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.01.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Face recognition system using steerable pyramid transform and local binary pattern is proposed. Zero-norm minimization and local learning based algorithms are used for feature selection. 99.28% accuracy was obtained in FERET database with fb set. Abstract: This paper proposes a face recognition system based on a steerable pyramid transform (SPT) and local binary pattern (LBP) for e-Health secured login. In an e-Health framework, patients are sometimes unable to identify themselves by traditional login modalities such as username and password. Automatic face recognition can replace the conventional login modalities if the recognition system is robust. In the proposed system, SPT can decompose a face image into several subbands of different scales and orientations, and LBP can encode the subbands in binary texture pattern. Therefore, SPT-LBP scheme represents a face image in a robust way that includes multiple information sources from different scales and orientations. The proposed system is evaluated on the facial recognition technology (FERET) database. According to the results, the proposed system achieves 99.28% recognition in fb set, 80.17% in dup I set, and 79.54% in dup II set. Graphical abstract: