Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis

Adak, C; Chaudhuri, BB; Lin, CT; Blumenstein, M

Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis

Adak, C

Chaudhuri, BB Lin, CT Blumenstein, M

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2020, 15, pp. 3567-3579
Issue Date:: 2020-01-01

Closed Access

	Filename	Description	Size
	09083953.pdf	Published version	2.01 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.contributor.author	Chaudhuri, BB
dc.contributor.author	Lin, CT
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2021-02-22T20:21:45Z
dc.date.available	2021-02-22T20:21:45Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2020, 15, pp. 3567-3579
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/146290
dc.description.abstract	© 2005-2012 IEEE. In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2020.2991833
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-22T20:21:42Z
pubs.publication-status	Published
pubs.volume	15

Abstract:

© 2005-2012 IEEE. In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146290