Learning algorithms for multi tasks of unconstrained fingerprint recognition

Abstract

Attributed to extensive acceptance and research, lives can and inked fingerprints are successfully used for human recognition. However, applications such as contactless biometrics amidst the pandemic and crime scene identification call for newer covariates of fingerprints. These new unconstrained/semi-constrained covariates consist of contactless fingerprint1, multi-view contactless fingerprints (contactless 3D or finger videos), and latent fingerprints. These fingerprints are prone to acquisition variations, resulting in a non-optimal performance with traditional fingerprint algorithms. These unconstrained fingerprints require dedicated algorithms that can yield state-of-the-art performance under different challenges. Hence, this thesis presents a six-fold contribution of recognition using semi/un-constrained fingerprints (finger photos, finger videos, and latent fingerprints). Structured in two parts, the first part discusses aspects of contactless fingerprints, while the second focuses on latent fingerprints. The thesis provides large-scale databases for different applications of unconstrained fingerprints. Using the databases, dedicated end-to-end recognition algorithms are presented for contactless fingerprints, fingervideos, and latent fingerprints. The recognition algorithm for latent fingerprints is further backed by scientific learning of the process followed by forensic examiners. The details of each of the contributions are listed below. The first three contributions in Part I of the thesis are towards contactless fingerprint recognition. As the first contribution, we create databases to establish large-scale contactless fingerprint recognition under various acquisition challenges and vulnerabilities. Primarily, the proposed IIIT-D Smart Phone Finger-selfie Database v2 (ISPFD-v2) accounts for 19,456 images in total. These databases understand the behavior of finger selfie recognition under various vulnerabilities and challenges.