Deep neural networks based contactless fingerprint recognition

For developing automatic and accurate system for human recognition, deep learning is now progressively becoming common in real-world biometrics applications. Fingerprint is one of the most important discriminative biometric characteristic due to its high reliability and uniqueness properties, which has led to a widespread use by law enforcement, forensic as well as in mobile devices user authentication. Contactless fingerprint recognition has achieved rapid development in recent years thanks to more hygienic and ubiquitous personal identification techniques. In this paper, we present deep neural networks (DNNs) based solutions for contactless fingerprint identification. More specifically, we show how existing DNNs can be deployed as a feature extractor for contactless fingerprint. Experimental analyses on publically available dataset with 336 subjects demonstrate the effectiveness of DNNs-based feature extractors. Moreover, experimental results illustrate best recognition performance in comparison with state-of-the-art texture descriptors.