Title
Live fingerprint detection using magnitude of perceived spatial stimuli and local phase information.
Abstract
Fingerprint recognition systems are widely used for authentication purposes in security systems. However, fingerprint recognition systems can easily be spoofed by imitations of fingerprints using various spoof materials. A compact and discriminative set of features is needed to discriminate between live and spoof fingerprints. We explore combined Shepard magnitude and orientation for live fingerprint detection using independent quantization of global and local features extracted in spatial and frequency domain. The spatial domain features that are extracted comprise of the magnitude of perceived spatial stimuli that is computed from the net variation of perceived edge information. Rotation invariance is achieved by extracting local features based on phase information of significant frequency components in the frequency domain. The concatenated feature vector associated with a fingerprint image is represented as a two-dimensional histogram. The support vector machine classifier is used to classify the fingerprint as either live or spoof. Experiments are performed on three databases, i.e., the fingerprint liveness detection (LivDet) competition databases of 2011, 2013, and 2015. Results showed a reduction in average error rate to 5.8, 2.2, and 5.3 on LivDet 2011, 2013, and 2015, respectively. (C) 2018 SPIE and IS&T
Year
DOI
Venue
2018
10.1117/1.JEI.27.5.053038
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
biometric systems,fingerprints,live,spoof,local and global features,perceived spatial stimuli,quantization,ridges valleys,ridge frequency
Computer vision,Magnitude (mathematics),Pattern recognition,Computer science,Fingerprint,Artificial intelligence,Stimulus (physiology)
Journal
Volume
Issue
ISSN
27
5
1017-9909
Citations 
PageRank 
References 
0
0.34
40
Authors
6
Name
Order
Citations
PageRank
Rubab Mehboob100.68
Hassan Dawood26714.45
Hussain Dawood35312.90
Muhammad Usman Ilyas411313.31
Ping Guo560185.05
Ameen Banjar611.36