Suraiya Jabin

Dr. Suraiya Jabin

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2020 in Journal of King Saud University Computer and Information Sciences
Verified
Journal Article
Open access
KSKashish Shakil
Kashish Shakil
FZFarhana J. Zareen
Farhana J. Zareen
MAMansaf Alam
Mansaf Alam
Suraiya JabinSuraiya Jabin
Suraiya Jabin

24 February 2022 in Pattern Recognition
Verified
Journal Article
Open access
RTRuben Tolosana
Ruben Tolosana
RVRuben Vera-Rodriguez
Ruben Vera-Rodriguez
CGCarlos Gonzalez-Garcia
Carlos Gonzalez-Garcia
Julian FierrezJulian Fierrez
Julian Fierrez
AMAythami Morales
Aythami Morales
Suraiya JabinSuraiya Jabin
Suraiya Jabin

24 December 2023 in International Journal of Information Technology
Verified
Journal Article
Closed access
PDF file
SMSarthak Mishra
Sarthak Mishra
Suraiya JabinSuraiya Jabin
Suraiya Jabin

1 December 2018 in Telematics and Informatics
Verified
Journal Article
Closed access
MWMudasir Ahmad Wani
Mudasir Ahmad Wani
NANancy Agarwal
Nancy Agarwal
Suraiya JabinSuraiya Jabin
Suraiya Jabin
SHSyed Zeeshan Hussain
Syed Zeeshan Hussain


1 March 2016 in IET Biometrics
Verified
Journal Article
Open access
PDF file
FZFarhana Javed Zareen
Farhana Javed Zareen
Suraiya JabinSuraiya Jabin
Suraiya Jabin
This is an undeniable fact that in the coming years a considerable percentage of organisations are drifting toward mobile devices for authentication. Banking sector as an additional offshoot has shifted to mobile devices with their applications for e‐banking and mobile‐banking, giving rise to an emergent requirement of a foolproof and authentic mobile‐biometric system. This study presents an authentic mobile‐biometric signature verification system and a comparative analysis of the performance of the proposed system for the two datasets; one using the standard device that is used for capturing biometric signatures and the other one is a mobile database taken from a smart phone for biometric signature authentication. The results presented demonstrate that the proposed system outperforms existing mobile‐biometric signature verification systems based on dynamic time warping and hidden Markov model. Moreover, this study presents a comprehensive survey of mobile‐biometric systems, different devices and hardware needed to support mobile biometrics along with open issues and challenges faced by the mobile‐biometric systems. The experiments presented establish that the performance of mobile devices is low as compared with normal biometric signature capturing devices and the major reason the authors found is the absence of pen‐tilt angle information in the mobile device datasets.