Fingerprint liveness detection using gradient-based texture features

• Published in: Signal, image and video processing Vol. 11; no. 2; pp. 381 - 388
• Main Authors: Xia, Zhihua, Lv, Rui, Zhu, Yafeng, Ji, Peng, Sun, Huiyu, Shi, Yun-Qing
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2017; Springer Nature B.V
• Subjects: Computer Imaging; Computer Science; Feature extraction; Feature recognition; Image classification; Image detection; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Texture recognition; Vision; Biometrics; Image texture; Co-occurrence array; Fingerprint liveness detection; Image gradient
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-016-0936-z

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.