Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm

• Published in: Engineering applications of artificial intelligence Vol. 60; pp. 151 - 163
• Main Authors: Joardar, Sandip, Chatterjee, Amitava, Bandyopadhyay, Sanghamitra, Maulik, Ujjwal
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2017
• Subjects: Collaborative representation based classification; Enhanced multi-size patch based CRC; Modified interactive artificial bee colony algorithm; Palm-Dorsa Vein Pattern; Small sample size; Thermal imaging; Palm-Dorsa Vein Pattern; Collaborative representation based classification; Thermal imaging; Modified interactive artificial bee colony algorithm; Enhanced multi-size patch based CRC; Small sample size
• DOI: 10.1016/j.engappai.2017.02.002
• ISSN: 0952-1976

This paper proposes a novel method, Multi-Size patch based Collaborative Representation based Classification (CRC) strategy by Enhanced Ensemble Learning, for palm dorsa vein pattern (PDVP) based human recognition employing thermal imaging. This thermal PDVP imaging based human recognition methodology has been specifically employed to encounter the challenging crisis of intrusion posed by imposters. To address the Small Sample Size (SSS) problem, intrinsic to many biometric applications, each image is transformed into multiple patches, leading to an increase in the total number of samples. In a bid to make the classification strategy less sensitive to the choice of patch-size, the present paper proposes a new enhanced ensemble learning for the patch based CRC (PCRC) algorithm, where margin maximization is carried out using exponential squared loss minimization. This work also proposes how this loss minimization can be achieved by a stochastic optimization algorithm and solves this problem using artificial bee colony (ABC) algorithm. In this context, a new ABC variant, called modified interactive artificial bee colony (MI-ABC) algorithm, has also been proposed, which has been demonstrated to outperform the basic ABC and its several modern variants. The proposed methodology has been implemented on a well–structured real database, developed in our laboratory using real subjects, and the results obtained in implementation phase clearly demonstrate that our proposed method could outperform its several competitors and achieve substantially high classification rates, for the problem under consideration.