Selecting optimal classification features for SVM based elimination of incorrectly matched minutiae

Praveer Mansukhani; Venu Govindaraju

doi:10.1117/12.778684

17 March 2008 Selecting optimal classification features for SVM based elimination of incorrectly matched minutiae

Proceedings Volume 6944, Biometric Technology for Human Identification V; 69440U (2008) https://doi.org/10.1117/12.778684
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Rather than use arbitrary matching threshold values and a heuristic set of features while comparing minutiae points during the fingerprint verification process, we develop a system which considers only the optimal features, which contain the highest discriminative power, from a predefined feature set. For this, we use a feature selection algorithm which adds features, one at a time, till it arrives at an optimal feature set of the target size. The classifier is trained on this feature set, on a two class problem representing pairs of matched minutiae points belonging to fingerprints of same and different users. During the test phase, the system generates a number of candidate matched minutiae pairs; features from each of them are extracted and given to the classifier. Those that are incorrectly matched are eliminated from the scoring algorithm. We have developed a set of seven candidate features, and tested our system using the FVC 2002 DB1 fingerprint database. We study how feature sets of different sizes affect the accuracy of the system, and observe how additional features not necessarily would improve the performance of a classifier. This is illustrated in how using a 3 feature set gives us the most accurate system and using bigger feature sets cause a slight drop in accuracy.

Citation Download Citation

Praveer Mansukhani and Venu Govindaraju "Selecting optimal classification features for SVM based elimination of incorrectly matched minutiae", Proc. SPIE 6944, Biometric Technology for Human Identification V, 69440U (17 March 2008); https://doi.org/10.1117/12.778684

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available