Fingerprint liveness detection using gradient-based texture features. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Xia, Zhihua; Lv, Rui; Zhu, Yafeng; Ji, Peng; Sun, Huiyu; Shi, Yun-Qing
• Is Part Of: Signal, image and video processing. Volume 11:Issue 2(2017)
• Page Start: 381
• Page End: 388
• Publication Date: 2017-02
• Subjects: Biometrics -- Fingerprint liveness detection -- Image texture -- Image gradient -- Co-occurrence array; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0936-z
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods.