The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Personal identification from degraded and incomplete high resolution palmprints

A high resolution palmprint recognition system using full or partial, eventually degraded, palmprints is presented. Previous work on palmprint matching addressed mostly commercial applications, using low resolution images. However, in forensic scenarios, high resolution palmprints, although incomplete and/or degraded, are often used. Degradations may result from surface irregularities or impurities, which are often modelled as Gaussian or salt and pepper noise, as well as smearing of the palmprint because of hand sliding, which in this work is modelled as motion blur. The proposed system matches palmprints, full or partial, undegraded or subjected to one of the above degradations, against palmprints registered in a database. The proposed system extends previous work of the authors by adaptively selecting between two palmprint matching approaches, achieving better recognition results than either of the two individual strategies. The first approach relies on a motion blur compensation technique, while the second is based on a combination of the Fourier–Mellin transform with a modified phase-only correlation matching strategy. The presented results show that for sufficiently large palmprint areas the blur compensation technique works better, while for small-sized partial palmprints with large motion blur degradation values the second approach based on correlation is preferred