An incremental learning method for spoof fingerprint detection

Spoof fingerprint detectors based on static features are built by learning a set of live and fake fingerprint images. These learning-based spoof detectors cannot accurately classify new or untrained types of fakes. To handle this problem, the existing spoof detector should be incrementally trained on the new types of fakes. This paper proposes a new spoof detection framework to learn new types of fakes incrementally without retraining the existing spoof detector repeatedly. The proposed model discriminates the newly learned fakes without serious loss of performance for the previously learned fakes and at the same time provides promising detection results for the various types of fakes. The proposed spoof detector integrates multiple “experts,” each of which shares the same structure but is separately trained for a different set of fake fingerprints. To detect a new type of fake fingerprint, a new expert exclusively trained on the new fake type is integrated into the spoof detector. Each expert consists of multiple support vector machines (SVMs) applied by an incremental learning algorithm (Learn++.NC), where each SVM adopts one of three texture features for spoof detection. Experimental results show the superiority of the proposed method compared with other methods in various scenarios.