An overview of touchless 2D fingerprint recognition

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Level 3 Feature Based Fingerprint Identification

In this thesis, two novel schemes have been proposed: one scheme on dots and incipient ridges extraction and another scheme on matching using level 2 and level 3 features. Dots and incipient ridges are extracted by tracing valley. Starting points are found on the valley by analyzing the frequencies present in the fingerprint. Valleys are traced from the starting point using Fast Marching Method (FMM). An intensity based checking method is used for finding these feature points. Delaunay triangle has been constructed using level 2 feature. A novel algorithm of selecting compatible triangle pair from Delaunay triangle is proposed. A novel set of feature parameters are constructed by establishing spatial relation between minutiae and dots-and-incipient. Pore based matching has been performed using Robust Affine Iterative Closest Point algorithm. These extended features (dots, incipient ridges, and pores) are helpful for forensic experts. However, forensic experts deal with full-to-partial print matching of latent fingerprint. Hence, Full-to-partial fingerprint matching has been carried out. Partial print is constructed by cropping a window from a full fingerprint in two ways such as, non-overlapped cropping and random cropping. Form the experiment, it has been observed that random cropping based fingerprint has better accuracy than non-overlapped cropping. For performance evaluation of the proposed algorithm, two public databases have been used: NIST SD30 database and IIIT Delhi rural database. All images in SD30 are taken in constrained environment and images in IIIT database are taken in unconstrained environment. Feature level and score level fusion have been carried out for fusing different levels of feature. It has been observed that score level fusion shows better accuracy than feature level fusion