The paper presents a novel framework for the application of multiple features for handwritten data based identity recognition. Different types of features characterise different facets of the handwriting. We have designed a scheme for multiple feature based identity establishment using multi-kernel learning. A new formulation for multi-kernel learning using genetic algorithm has been presented. The efficacy of the framework using individual and combination of features is demonstrated for Devanagari script input. © 2013 Elsevier B.V. All rights reserved.

Madan Gopal

Electrical Engineering

(SoE) School of Engineering

Hassan E.

Chaudhury S.

Yadav N.

Kalra P.

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Pattern Recognition Letters

The paper presents a novel feature based indexing scheme for image collections. The scheme presents the extension of distance based hashing to kernel space for generating the indexing structure based on similarity in kernel space. The objective of the scheme is to incorporate multiple features for defining the image indexing space using the concept of multiple kernel learning. However, the indexing problems are defined with unique learning objective; therefore, a novel application of genetic algorithm is presented for the optimization task. The extensive evaluation of the proposed concept is performed for developing word based document indexing application of Devanagari, Bengali, and English scripts. In addition, the efficacy of the proposed concept is shown by experimental evaluations on handwritten digits and natural image collection. © 2012 IEEE.

IEEE Transactions on Multimedia

Feature combination in kernel space for distance based image hashing

2010 12th International Conference on Frontiers in Handwriting Recognition

Identity Determination with Offline Handwritten Input Using Multi Kernel Feature Combination

Pattern Recognition

Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features

A text-independent Persian writer identification based on feature relation graph (FRG)

Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers

Off-line hand written input based identity determination using multi kernel feature combination