Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

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

A Secure Template Generation Scheme for Palmprint Recognition Systems

With the development of more and more systems which provide service based on the identity of a person, the importance of personal identification is growing. Providing authorized users with secure access to the services is a challenge to the personal identification systems. There are several conventional means for personal identi¯cation which include passports, keys, tokens, access cards, personal identification number (PIN), passwords. Unfortunately, passports, keys, access cards, tokens, can be lost, stolen or duplicated, and passwords, PINs can be forgotten,cracked or shared. These drawbacks cause a great loss to the concerned. Biometric systems are proving to be an e±cient solution to this problem. A biometric identity verification system tries to verify user identities by comparing some sort of behavioral or physiological trait of the user to a previously stored sample of the trait. The recent developments in the biometrics area have lead to smaller, faster and cheaper systems, which in turn has increased the number of possible application areas for biometric identity verification. Palmprint can be one of the biometrics, used for personal identification or verification. As a small central part of the palmprint image is used for this purpose, so it is important to find that region of interest. We propose a rotation and translation invariant preprocessing scheme which finds the central part of the palmprints. As biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analyzed before they are massively deployed in security systems. Along with security, also the privacy of the users is an important factor as the constructions of lines in palmprints contain personal characteristics. We propose a cryptographic approach to encrypt the palmprint images by an advanced Hill cipher technique for hiding the information on the palmprints. It also provides security to the palmprint images from being attacked by above mentioned attacks. So, during the template generation, the encrypted palmprint sub-images are first decrypted and then the features are extracted

Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students