Face Spoof Attack Recognition Using Discriminative Image Patches
Joint Authors
Akhtar, Zahid
Foresti, Gian Luca
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-22
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
Face recognition systems are now being used in many applications such as border crossings, banks, and mobile payments.
The wide scale deployment of facial recognition systems has attracted intensive attention to the reliability of face biometrics against spoof attacks, where a photo, a video, or a 3D mask of a genuine user’s face can be used to gain illegitimate access to facilities or services.
Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty in finding discriminative and computationally inexpensive features and methods for spoof attacks.
In addition, existing techniques use whole face image or complete video for liveness detection.
However, often certain face regions (video frames) are redundant or correspond to the clutter in the image (video), thus leading generally to low performances.
Therefore, we propose seven novel methods to find discriminative image patches, which we define as regions that are salient, instrumental, and class-specific.
Four well-known classifiers, namely, support vector machine (SVM), Naive-Bayes, Quadratic Discriminant Analysis (QDA), and Ensemble, are then used to distinguish between genuine and spoof faces using a voting based scheme.
Experimental analysis on two publicly available databases (Idiap REPLAY-ATTACK and CASIA-FASD) shows promising results compared to existing works.
American Psychological Association (APA)
Akhtar, Zahid& Foresti, Gian Luca. 2016. Face Spoof Attack Recognition Using Discriminative Image Patches. Journal of Electrical and Computer Engineering،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1108444
Modern Language Association (MLA)
Akhtar, Zahid& Foresti, Gian Luca. Face Spoof Attack Recognition Using Discriminative Image Patches. Journal of Electrical and Computer Engineering No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1108444
American Medical Association (AMA)
Akhtar, Zahid& Foresti, Gian Luca. Face Spoof Attack Recognition Using Discriminative Image Patches. Journal of Electrical and Computer Engineering. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1108444
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108444