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With the development of presentation attacks, Automated FingerprintRecognition Systems(AFRSs) are vulnerable to presentation attack. Thus,numerous methods of presentation attack detection(PAD) have been proposed toensure the normal utilization of AFRS. However, the demand of large-scalepresentation attack images and the low-level generalization ability alwaysastrict existing PAD methods' actual performances. Therefore, we propose anovel Zero-Shot Presentation Attack Detection Model to guarantee thegeneralization of the PAD model. The proposed ZSPAD-Model based on generativemodel does not utilize any negative samples in the process of establishment,which ensures the robustness for various types or materials based presentationattack. Different from other auto-encoder based model, the Fine-grained Maparchitecture is proposed to refine the reconstruction error of the auto-encodernetworks and a task-specific gaussian model is utilized to improve the qualityof clustering. Meanwhile, in order to improve the performance of the proposedmodel, 9 confidence scores are discussed in this article. Experimental resultsshowed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Scoreis the best confidence score. Compared with existing methods, the proposedZSPAD-Model performs better than the feature-based method and under themulti-shot setting, the proposed method overperforms the learning based methodwith little training data. When large training data is available, their resultsare similar.

A Zero-Shot based Fingerprint Presentation Attack Detection System