Fingerprint liveness detection using local quality features

Fingerprint-based recognition is widely deployed in different domains. However, current recognition systems are vulnerable to presentation attack. Presentation attack utilizes an artificial replica of a fingerprint to deceive the sensors. In such scenarios, fingerprint liveness detection is required to ensure the actual presence of a live fingerprint. In this paper, we propose a static software-based approach using quality features to detect the liveness in a fingerprint image. The proposed method extracts eight sensor-independent quality features from the detailed ridge-valley structure of a fingerprint at the local level to form a 13-dimensional feature vector. Sequential Forward Floating Selection and Random Forest Feature Selection are used to select the optimal feature set from the created feature vector. To classify fake and live fingerprints, we have used support vector machine, random forest, and gradient boosted tree classifiers. The proposed method is tested on a publically available database of LivDet 2009 competition. The experimental results demonstrate that the least average classification error of 5.3% is achieved on LivDet 2009 database, exhibiting supremacy of the proposed method over current state-of-the-art approaches. Additionally, we have analyzed the importance of individual features on LivDet 2009 database, and effectiveness of the best-performing features is evaluated on LivDet 2011, 2013, and 2015 databases. The obtained results depict that the proposed approach is able to perform well irrespective of the different sensors and materials used in these databases. Further, the proposed method utilizes a single fingerprint image. This characteristic makes our method more user-friendly, faster, and less intrusive.