Fingerprints clustering with unsupervised deep learning.

Fingerprint is one of the most famous biometric characteristics. It is employed in many fields such as forensic, security, recognition and classification. This paper focuses on clustering fingerprint images into original and fake. Unsupervised Deep Leaning (UDL) is proposed, it exploits the Self-Organization Maps (SOM) to provide such clustering. It consists of two internal processing parts. The first part is for the feature extraction. The second part is for the unsupervised clustering of the SOM. Fingerprint images from the ATVS-FakeFingerprint DataBase (ATVS-FFpDB) for without cooperation are utilized in our work. Multiple clustering and classification metrics of the Silhouette Value (SV), Calinski Harabasz Index (CHI), Davies-Bouldin Index (DBI) and accuracy are provided. Also, different comparisons with state-of-the-art Deep Learning (DL) architectures are provided. Our UDL approach has achieved a high accuracy result of 92.86% and fingerprint images are successfully clustered into original and fake categories. [ABSTRACT FROM AUTHOR]