Aging in Biometrics: An Experimental Analysis on On-Line Signature

The first consistent and reproducible evaluation of the effect of aging on dynamic signature is reported. Experiments are carried out on a database generated from two previous datasets which were acquired, under very similar conditions, in 6 sessions distributed in a 15-month time span. Three different systems, representing the current most popular approaches in signature recognition, are used in the experiments, proving the degradation suffered by this trait with the passing of time. Several template update strategies are also studied as possible measures to reduce the impact of aging on the system’s performance. Different results regarding the way in which signatures tend to change with time, and their most and least stable features, are also given.

[1]  Javier Garrido Salas,et al.  BiosecurID: a multimodal biometric database , 2009, Pattern Analysis and Applications.

[2]  Richard Guest.  19794-11 Information technology -- Biometric Data Interchange Formats -- Part 11: Signature/sign processed dynamic data. , 2013 .

[3]  Valentín Cardeñoso-Payo,et al.  BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures , 2012, Pattern Recognit..

[4]  Julian Fiérrez,et al.  HMM-based on-line signature verification: Feature extraction and signature modeling , 2007, Pattern Recognit. Lett..

[5]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Niels da Vitoria Lobo,et al.  Age Classification from Facial Images , 1999, Comput. Vis. Image Underst..

[7]  Berrin A. Yanikoglu,et al.  Identity authentication using improved online signature verification method , 2005, Pattern Recognit. Lett..

[8]  Hong Chang,et al.  SVC2004: First International Signature Verification Competition , 2004, ICBA.

[9]  Arun Ross,et al.  Revisiting Doddington"s Zoo: A Systematic Method to Assess User-dependent Variabilities , 2006 .

[10]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Transactions on Image Processing.

[11]  Stefano Soatto,et al.  A Study of Face Recognition as People Age , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  A. Albert,et al.  A review of the literature on the aging adult skull and face: implications for forensic science research and applications. , 2007, Forensic science international.

[13]  Richard M. Guest.  Age dependency in handwritten dynamic signature verification systems , 2006, Pattern Recognit. Lett..

[14]  John W. Carls.  A Framework for Analyzing Biometric Template Aging and Renewal Prediction , 2012 .

[15]  A. Rattani,et al.  Biometric template update using the graph mincut algorithm : A case study in face verification , 2008, 2008 Biometrics Symposium.

[16]  A. Anthony Irudhayaraj,et al.  Biometric system , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[17]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[18]  C. Christodoulou,et al.  Comparing different classifiers for automatic age estimation , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Marcos Faúndez-Zanuy,et al.  On the relevance of age in handwritten biometric recognition , 2012, 2012 IEEE International Carnahan Conference on Security Technology (ICCST).

[20]  Rajiv Grover,et al.  The anatomy of the aging face: volume loss and changes in 3-dimensional topography. , 2006, Aesthetic surgery journal.

[21]  Song-Chun Zhu,et al.  A Multi-Resolution Dynamic Model for Face Aging Simulation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Hakil Kim,et al.  Impact of Age Groups on Fingerprint Recognition Performance , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[23]  Andreas Lanitis,et al.  Evaluating the performance of face-aging algorithms , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[24]  Richard Guest,et al.  Information technology -- 29107-7 Conformance testing methodology for biometric data interchange formats defined in ISO/IEC 19794 -- Part 7: Signature/sign time series data , 2011 .

[25]  R. Dixon,et al.  Handwriting performance in younger and older adults: age, familiarity, and practice effects. , 1993, Psychology and aging.

[26]  Loris Nanni,et al.  A clustering method for automatic biometric template selection , 2006, Pattern Recognit..

[27]  Julian Fiérrez,et al.  Performance and robustness: A trade-off in dynamic signature verification , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  Gian Luca Marcialis,et al.  Template Update Methods in Adaptive Biometric Systems: A Critical Review , 2009, ICB.

[29]  Michael C. Fairhurst,et al.  Framework for managing ageing effects in signature biometrics , 2012, IET Biom..

[30]  S. Modi,et al.  Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints , 2006 .

[31]  Natalia Dounskaia,et al.  Control of multijoint drawing movements: a comparison of young and elderly adults , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[32]  Mark S. Nixon,et al.  Model-based approaches for predicting gait changes over time , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[33]  Annie McCluskey,et al.  A review of factors that influence adult handwriting performance. , 2011, Australian occupational therapy journal.

[34]  Anil K. Jain,et al.  Encyclopedia of Biometrics , 2015, Springer US.

[35]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Julian Fiérrez,et al.  Biosec baseline corpus: A multimodal biometric database , 2007, Pattern Recognit..

[37]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[38]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[39]  Andreas Lanitis,et al.  A survey of the effects of aging on biometric identity verification , 2010, Int. J. Biom..

[40]  Arun Ross,et al.  Biometric template selection and update: a case study in fingerprints , 2004, Pattern Recognit..

[41]  F. Alonso-Fernandez,et al.  Impact of time variability in off-line writer identification and verification , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[42]  Ibon Saratxaga,et al.  Detection of synthetic speech for the problem of imposture , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[43]  Karl Ricanek,et al.  MORPH: Development and Optimization of a Longitudinal Age Progression Database , 2009, COST 2101/2102 Conference.

[44]  Douglas A. Reynolds,et al.  SHEEP, GOATS, LAMBS and WOLVES A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation , 1998 .

[45]  Hugo Van hamme,et al.  Age Estimation from Telephone Speech using i-vectors , 2012, INTERSPEECH.

[46]  Yiying Tong,et al.  Age-Invariant Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Bernadette Dorizzi,et al.  On assessing the robustness of pen coordinates, pen pressure and pen inclination to time variability with personal entropy , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[48]  J Walton.  Handwriting changes due to aging and Parkinson's syndrome. , 1997, Forensic science international.

[49]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Mireille Avigal,et al.  Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the Acoustic Speech Signal , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[51]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[52]  Gian Luca Marcialis,et al.  Template Co-update in Multimodal Biometric Systems , 2007, ICB.

[53]  Anil K. Jain,et al.  On-line signature verification, , 2002, Pattern Recognit..

[54]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[55]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[56]  Claus Vielhauer,et al.  Short Term Template Aging Effects on Biometric Dynamic Handwriting Authentication Performance , 2012, Communications and Multimedia Security.

[57]  Julian Fiérrez,et al.  Improving the Enrollment in Dynamic Signature Verfication with Synthetic Samples , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[58]  Loris Nanni,et al.  An On-Line Signature Verification System Based on Fusion of Local and Global Information , 2005, AVBPA.

[59]  Alejandro F. Frangi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. , 2022 .

[60]  Christian O'Reilly,et al.  Design of a neuromuscular disorders diagnostic system using human movement analysis , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[61]  Jana Dittmann,et al.  Comparative review of studies on aging effects in context of biometric authentication , 2011, Electronic Imaging.