Feature Level Fusion of Iris and Fingerprint Biometrics for personal identification using Artificial Neural Network

This research presents the multi –modal biometric system for iris and fingerprint This paper presents the Feature level fusion using wavelet for combining two unimodal biometric system. Gabor transform is used for feature extraction and wavelet transformation for fusion of iris and fingerprint. The system applied artificial neural network technique for recognizing whether the user is genuine (accepted) or impostor (rejected). The proposed system is for multimodal database comprising of 20 samples. The performance of the system is tested on a database prepared to find accuracy, false acceptance rate and false rejection rate. DOI: 10.17762/ijritcc2321-8169.15077

Efficent Approach Use to Increase accuracy Multimodal Biometric System for Feature Level Fusion

Security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process.In this paper we propose a Efficient and Robust Multimodal Biometric System for feature level fusion that combines the information to investigate whether the integration of fingerprints and signatures . Proposed system extracts Gabor texture from the preprocessed fingerprints and signatures sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.proposed approach is authenticated for their accuracy on Fingerprints virtual database fused with signature virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.8% and with false rejection rate (FRR) of = 1

Approach to Increase Accuracy of Multimodal Biometric System for Feature Level Fusion

Biometric System are alternates to the traditional identification system. The Paper provides the multiple features based on the biometric system including Physiological and behaviouralchractersitics.like Fingerprints and iris which is used to identify the Fake and Genuine Users..In this paper we propose a Multimodal Biometric System for feature level fusion that combines the information to investigate the integration of fingerprints and Iris . This Proposed system extracts Gabor texture from the preprocessed fingerprints and Iris sample. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore proposed the wavelet-based fusion techniques. Finally apply neural network’s Cascaded feed forward Back propagation Algorithm to Train Neurons for recognition.This approach is authenticated for their accuracy of Fingerprints virtual database fused with Iris virtual database of 16 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a accuracy of 99.53% and with false rejection ratio (FRR) of = 1

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit

Hand-based multimodal identification system with secure biometric template storage

WOS:000304107200001This study proposes a biometric system for personal identification based on three biometric characteristics from the hand, namely: the palmprint, finger surfaces and hand geometry. A protection scheme is applied to the biometric template data to guarantee its revocability, security and diversity among different biometric systems. An error-correcting code (ECC), a cryptographic hash function (CHF) and a binarisation module are the core of the template protection scheme. Since the ECC and CHF operate on binary data, an additional feature binarisation step is required. This study proposes: (i) a novel identification architecture that uses hand geometry as a soft biometric to accelerate the identification process and ensure the system's scalability; and (ii) a new feature binarisation technique that guarantees that the Hamming distance between transformed binary features is proportional to the difference between their real values. The proposed system achieves promising recognition and speed performances on two publicly available hand image databases.info:eu-repo/semantics/acceptedVersio

Robust iris recognition under unconstrained settings

Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

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