Paper Info
Title
Matching Fingerphotos to Slap Fingerprint Images.
Abstract
We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images. Development of robust versions of these technologies would enable the use of the billions of standard Android phones as biometric readers through a simple software download, dramatically lowering the cost and complexity of deployment relative to using a separate fingerprint reader. Two fingerphoto apps running on Android phones and an optical slap reader were utilized for fingerprint collection of 309 subjects who primarily work as construction workers, farmers, and domestic helpers. Experimental results show that a True Accept Rate (TAR) of 95.79 at a False Accept Rate (FAR) of 0.1% can be achieved in matching fingerphotos to slaps (two thumbs and two index fingers) using a COTS fingerprint matcher. By comparison, a baseline TAR of 98.55% at 0.1% FAR is achieved when matching fingerprint images from two different contact-based optical readers. We also report the usability of the two smartphone apps, in terms of failure to acquire rate and fingerprint acquisition time. Our results show that fingerphotos are promising to authenticate individuals (against a national ID database) for banking, welfare distribution, and healthcare applications in developing countries.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Computer vision,Software download,Software deployment,Authentication,Android (operating system),Fingerprint recognition,Computer science,Usability,Fingerprint,Artificial intelligence,Biometrics,Machine learning
DocType
Volume
Citations 
Journal
abs/1804.08122
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Debayan Deb1647.25
Tarang Chugh2455.62
Joshua J. Engelsma3225.78
Kai Cao420718.68
Neeta Nain56225.32
Jake Kendall600.34
Anil Jain7335073334.84