Anti-spoofing for display and print attacks on palmprint verification systems

Vivek Kanhangad; Shruti Bhilare; Pragalbh Garg; Pranjalya Singh; Narendra Chaudhari

doi:10.1117/12.2180333

15 May 2015 Anti-spoofing for display and print attacks on palmprint verification systems

Vivek Kanhangad, Shruti Bhilare, Pragalbh Garg, Pranjalya Singh, Narendra Chaudhari

Proceedings Volume 9457, Biometric and Surveillance Technology for Human and Activity Identification XII; 94570E (2015) https://doi.org/10.1117/12.2180333
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

A number of approaches for personal authentication using palmprint features have been proposed in the literature, majority of which focus on improving the matching performance. However, of late, preventing potential attacks on biometric systems has become a major concern as more and more biometric systems get deployed for wide range of applications. Among various types of attacks, sensor level attack, commonly known as spoof attack, has emerged as the most common attack due to simplicity in its execution. In this paper, we present an approach for detection of display and print based spoof attacks on palmprint verifcation systems. The approach is based on the analysis of acquired hand images for estimating surface re ectance. First and higher order statistical features computed from the distributions of pixel intensities and sub-band wavelet coeefficients form the feature set. A trained binary classifier utilizes the discriminating information to determine if the acquired image is of real hand or a fake one. Experiments are performed on a publicly available hand image dataset, containing 1300 images corresponding to 230 subjects. Experimental results show that the real hand biometrics samples can be substituted by the fake digital or print copies with an alarming spoof acceptance rate as high as 79.8%. Experimental results also show that the proposed spoof detection approach is very effective for discriminating between real and fake palmprint images. The proposed approach consistently achieves over 99% average 10-fold cross validation classification accuracy in our experiments.

Citation Download Citation

Vivek Kanhangad, Shruti Bhilare, Pragalbh Garg, Pranjalya Singh, and Narendra Chaudhari "Anti-spoofing for display and print attacks on palmprint verification systems", Proc. SPIE 9457, Biometric and Surveillance Technology for Human and Activity Identification XII, 94570E (15 May 2015); https://doi.org/10.1117/12.2180333

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