2017
DOI: 10.1007/s11042-017-4475-6
|View full text |Cite
|
Sign up to set email alerts
|

DeepKnuckle: revealing the human identity

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
25
0

Year Published

2017
2017
2020
2020

Publication Types

Select...
6
3

Relationship

2
7

Authors

Journals

citations
Cited by 35 publications
(25 citation statements)
references
References 33 publications
0
25
0
Order By: Relevance
“…CASIA, Chinese Academy of Sciences Institute of Automation; CMC, critical micelle concentration; CRR, correct recognition rate; FAR; …; FKP, finger-knuckle-print; FRR, …; IITD, Indian Institute of Technology Delhi; LI, left index; LM, left middle; RI, right index; RM, right middle; ROC, receiver operating characteristics; PolyU, Polytecthnic University recognition are superior than state of art unimodal (Morales et al, 2011;Nigam et al, 2016;Zhang et al, 2003) palm print approaches. Likewise, finger knuckle demonstrates the outstanding performance of proposed approach than state of art unimodal (Gao, Yang, Qian, & Zhang, 2014;Jaswal, Nigam, & Nath, 2017b;Nigam et al, 2016;Zhang et al, 2009) approaches. Aside, the purposed multimodal approach shows optimum fusion results on a virtual combination of CASIA palm print and PolyU FKP data sets in comparison with the virtual combination of IITD palm print and PolyU FKP databases.…”
Section: Comparison With State-of-art Methodsmentioning
confidence: 90%
See 1 more Smart Citation
“…CASIA, Chinese Academy of Sciences Institute of Automation; CMC, critical micelle concentration; CRR, correct recognition rate; FAR; …; FKP, finger-knuckle-print; FRR, …; IITD, Indian Institute of Technology Delhi; LI, left index; LM, left middle; RI, right index; RM, right middle; ROC, receiver operating characteristics; PolyU, Polytecthnic University recognition are superior than state of art unimodal (Morales et al, 2011;Nigam et al, 2016;Zhang et al, 2003) palm print approaches. Likewise, finger knuckle demonstrates the outstanding performance of proposed approach than state of art unimodal (Gao, Yang, Qian, & Zhang, 2014;Jaswal, Nigam, & Nath, 2017b;Nigam et al, 2016;Zhang et al, 2009) approaches. Aside, the purposed multimodal approach shows optimum fusion results on a virtual combination of CASIA palm print and PolyU FKP data sets in comparison with the virtual combination of IITD palm print and PolyU FKP databases.…”
Section: Comparison With State-of-art Methodsmentioning
confidence: 90%
“…Although fingerprint is highly discriminable and perhaps the most successful form of hand‐based authentication mechanism (Cappelli, Ferrara, & Maltoni, 2010). But the quality of fingerprint can easily be damaged for cultivators and labourers due to regular hand usages in the day to day works (Jaswal, Nigam, & Nath, 2017b). On the contrary, the quality of finger knuckle (Jaswal, Nigam, & Nath, 2017b) and palm print (Zhang, Kong, You, & Wong, 2003) remain good throughout the person's life.…”
Section: Introductionmentioning
confidence: 99%
“…In fact, many different biometric traits have been investigated widely such as fingerprint, iris, ear, finger knuckle print, palm print, face etc. (Chaa et al 2017;Jaswal et al 2017a). Recently, finger knuckle print (FKP) (Cappelli et al 2010), which is included in the hand based biometric traits, have been studied in order to improve the consistent authentication system with higher accuracy (Jaswal et al 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Current Iris Recognition systems obtain excellent results by controlling the quality of images captured for recognition and by imposing certain constraints like making the user stand at a close and fixed distance from the camera [1] and so on. However, these constraints are not suitable in various real‐world applications such as airport boarding and control access because in those situations, when the acquisition is being made the subject is at a large distance with some possible movement [1, 2]. In such non‐ideal conditions, the captured iris images suffer from various degradation such as high occlusions, motion blur, low contrast, and lack of resolution [3].…”
Section: Introductionmentioning
confidence: 99%