Unconstrained Fingerphoto Database

Biometrics based user authentication for mobile devices is now popular with face and fingerprints being the primary modalities. Fingerphoto, an image of a person's finger captured using inbuilt smartphone camera, based user authentication is an attractive and cost-effective alternative. Existing research focuses on constrained or semi-constrained environment; whereas, challenges such as user cooperation, number of fingers, background, orientation, and deformation are important to address before fingerphoto authentication becomes usable. This paper presents the first publicly available unconstrained fingerphoto database, termed as UNconstrained FIngerphoTo (UNFIT) database, which contains fingerphoto images acquired in unconstrained environments. We also present baseline results with deep learning based segmentation as well as CompCode and ResNet50 representation based matching approaches. We assert that the availability of the proposed database can encourage research in this important domain.