Fingerprint Fusion Research Articles

Cryptographic Algorithm based Feature Level Fusion of Fingerprint and Iris in a Multi-Biometric Recognition System

Biometric encryption is one of the developing exploration area, which is a strategy for merging biometric features with cryptographic keys. Biometric Recognition is based on the anatomical and behavior attributes of the individuals. Multibiometric is the combination of various biometrics like Fingerprint, Iris, and Face, Fingervein etc. Experts are concentrating on the most proficient method to give security to the framework, the template which was produced from the biometric should be ensured. The main objective of this paper is to protect the multi biometric template by creating a protected sketch by deploying bio cryptosystem. Once the biometric template is stolen it turns into a major issue for the security of the framework and furthermore for client protection. In this way, a bio-crypto framework ensures the confidentiality of the information. In this paper bio cryptosystem is proposed to improve the security of multimodal frameworks by producing the biocrypto key from Finger print and iris. Gray level co-occurrence matrix (GLCM) based Haralick features, local binary pattern (LBP), triplet half-band filter bank (THFB) and dynamic features (DF) are extracted from fingerprint and iris. The high dimensionality space of the features are reduced using kernel principal component analysis (KPCA. Finally, the encoding process is matted with biometric key utilizing symmetric RSA (Rivest-Shamir-Adleman) cryptographic algorithm.

Multimodal biometrics with serial, parallel and hierarchical mode at decision level fusion

<p>Biometric based personal authentication is playing a vital role in various security based applications. This paper presents the effective fusion of fingerprint, palmprint and iris traits at decision level. Combining different traits at the decision level is a challenging task due to less information available at this level. The focus of the work is to examine the performance of multimodal biometrics at decision level fusion in three different i.e. serial, parallel and hierarchical modes of operation. Serial mode is performed by taking unimodals serially while parallel mode of operation is carried out by processing all modals simulatenously using Majority Voting Rule and the hierarchical mode of operation is performed with proper combination of traits in parallel and serial mode using AND and OR rule. The experiments are performed on 100 different users from publically available FVC2006 fingerprint database, CASIA V1 palmprint database and IITD iris database. The experimental results suggest that proper fusion of different traits in hierarchical way can give best performance even at decision level fusion as compared to serial and parallel mode of operation.</p>

Novel Local Coding Algorithm for Finger Multimodal Feature Description and Recognition.

Recently, finger-based biometrics, including fingerprint (FP), finger-vein (FV) and finger-knuckle-print (FKP) with high convenience and user friendliness, have attracted much attention for personal identification. The features expression which is insensitive to illumination and pose variation are beneficial for finger trimodal recognition performance improvement. Therefore, exploring suitable method of reliable feature description is of great significance for developing finger-based biometric recognition system. In this paper, we first propose a correction approach for dealing with the pose inconsistency among the finger trimodal images, and then introduce a novel local coding-based feature expression method to further implement feature fusion of FP, FV, and FKP traits. First, for the coding scheme a bank of oriented Gabor filters is used for direction feature enhancement in finger images. Then, a generalized symmetric local graph structure (GSLGS) is developed to fully express the position and orientation relationships among neighborhood pixels. Experimental results on our own-built finger trimodal database show that the proposed coding-based approach achieves excellent performance in improving the matching accuracy and recognition efficiency.

Secured Biometric Identification: Hybrid Fusion of Fingerprint and Finger Veins

Secured Biometric Identification: Hybrid Fusion of Fingerprint and Finger Veins

Image Fusion of Fingerprints using Fractal Representation and Dual Tree Complex Wavelet Transform

Image Fusion of Fingerprints using Fractal Representation and Dual Tree Complex Wavelet Transform

Data Fusion of ion Mobility Spectrometry Combined with Hierarchical Clustering Analysis for the Quality Assessment of Apple Essence

A fingerprinting approach was developed by means of high-performance ion mobility spectrometry with direct electrospray ionization (ESI-HPIMS) for the quality consistency and authentication of apple essence which contain many analytes. Thirty-four apple essence samples of the same brand and commercialized as a same product but brewed in four different manufacturers were used to establish the fingerprints. Hierarchical clustering analysis (HCA) was performed to evaluate the similarity and variation of these samples. A combined data matrix was constructed with the use of individual data matrix of ion mobility spectrum in positive and negative ion modes. For comparison, ion mobility spectrum fingerprints in positive and negative ion modes respectively were also applied to the quality assessment of the same samples. Finally, our study demonstrated that the fusion of fingerprints did indeed provide more information and could be used to comprehensively conduct the quality consistency evaluation and discrimination of apple essences from similar products. It is suggested that this fingerprint approach is suitable for analysis of other complex, multianalyte substances.

A Review on Iris and Fingerprint Fusion Authentication Systems

A Review on Iris and Fingerprint Fusion Authentication Systems

DPD-DFF: A dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases

Nowadays, many companies and institutions need fast and reliable identification systems that are able to deal with very large databases. Fingerprints are among the most used biometric traits for identification. In the current literature there are fingerprint matching algorithms that are focused on efficiency, whilst others are based on accuracy. In this paper we propose a flexible dual phase identification method, called DPD-DFF, that combines two fingers and two matchers within a hybrid fusion scheme to obtain both fast and accurate results. Different alternatives are designed to find a trade-off between runtime and accuracy that can be further tuned with a single parameter. The experiments show that DPD-DFF obtains very competitive results in comparison with the state-of-the-art score fusion techniques, especially when dealing with large databases or impostor fingerprints.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of liveness-recognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay-Attack Database and CASIA Face Anti-Spoofing Database) and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

Score Level Fusion for Fingerprint, Iris and Face Biometrics

Single biometric systems suffer from many challenges such as noisy data, non-universality and spoof attacks. Multimodal biometric systems can solve these limitations effectively by using two or more individual modalities. In this paper fusion of fingerprint, iris and face traits are used at score level in order to improve the accuracy of the system. Scores which obtained from the classifiers are normalized first using minmax normalization. Then sum, product and weighted sum rules are used to get fusion. Experimental results show that multimodal biometric systems outperform unimodal biometric systems and weighted sum rule gives the best results comparing with sum or product method.

Fusion of Fingerprint and Age Biometric for Gender Classification Using Frequency and Texture Analysis

Fusion of Fingerprint and Age Biometric for Gender Classification Using Frequency and Texture Analysis

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

Abstract In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The ‘best integration weights’ are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

Optimal score level fusion combining multi-normalisation and separability measures

This paper demonstrates the utility of multi-normalisation and separability measures for the optimal fusion of fingerprint and speaker biometrics. The decision scores of the individual matchers are transformed using various normalisation techniques and the global scores are obtained by combining the multi-normalised scores using the weighted fusion rules. The class as well as the score separability measures, under various noise conditions are estimated and combined algebraically, to determine the best integration weight, for the complementary modalities employed. The weight factor is optimised against the recognition accuracy. Experiments done with chimeric user database result in minimising the intersection between the genuine and the impostor score distributions, which in turn reduces the classification errors. Hence, by incorporating multi-normalisation and integration weight optimisation scheme on a unified framework, we can achieve better recognition performance and make the system robust to fluctuating inputs, even under extreme noise conditions.

Iris and Fingerprint Fusion for Biometric Identification

Iris and Fingerprint Fusion for Biometric Identification

An ECC Based Multibiometric System for Enhancing Security

Authentication schemes employing multibiometric system begin with several identification and verification based security schemes. Current systems implementing multi biometric scheme remain unsatisfactory facing to the growing security needs. The proposed system provides secured authentication to integrate multi biometric system with Elliptic Curve Cryptography that employs two modalities (i) Fusion of fingerprint, face, iris and signature. (ii) Generating elliptic curve and key using Elliptic Curve Cryptography. In the proposed system, selected portion of multi biometric features are fused into a single image and a curve is generated using ECC based technique that employs secured domain parameters generated through Genetic Algorithm. A one time password is also appended to the system to afford high authentication. The proposed algorithm is highly efficient against False Acceptance Rate and False Rejection Rate.

An ECC Based Multibiometric System for Enhancing Security

Authentication schemes employing multibiometric system begin with several identification and verification based security schemes. Current systems implementing multi biometric scheme remain unsatisfactory facing to the growing security needs. The proposed system provides secured authentication to integrate multi biometric system with Elliptic Curve Cryptography that employs two modalities (i) Fusion of fingerprint, face, iris and signature. (ii) Generating elliptic curve and key using Elliptic Curve Cryptography. In the proposed system, selected portion of multi biometric features are fused into a single image and a curve is generated using ECC based technique that employs secured domain parameters generated through Genetic Algorithm. A one time password is also appended to the system to afford high authentication. The proposed algorithm is highly efficient against False Acceptance Rate and False Rejection Rate.

Adaptive score level fusion of fingerprint and voice combining wavelets and separability measures

This paper presents an adaptive combinational approach for the score level fusion of fingerprint and voice biometrics, whose performance under adverse noise conditions are investigated systematically. An efficient preprocessing on the raw vector of scores using normalization and wavelet denoising is proposed, to improve the performance of the multibiometric system. The class as well as the score separability measures, under various noise conditions are estimated and combined algebraically, to determine the best integration weights, for the complementary modalities employed. The z-score normalized impostor scores are modelled as white Gaussian noise so that the wavelet denoising techniques can be readily applied. The inter/intra class separability measures from the feature space and the d-prime separability measures from the match score space are estimated in the training/validation phase. The performance of the proposed method is compared with the baseline techniques on score level fusion. Experimental evaluations show that the proposed method improves the recognition accuracy and reduces the false acceptance rate (FAR) and false rejection rate (FRR) over the baseline systems, under various signal-to-noise ratio (SNR) conditions. The proposed biometric solutions will be extremely useful in applications where there are less number of available training samples.

Dynamic Weighting for Effective Fusion of Fingerprint and Finger Vein

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A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

Biometric based person identity verification is gaining more and more attention. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and reliability compared with recognition using a single biometric. The present paper introduces a new personal identification framework that is based on the fusion of face and fingerprint biometrics. The proposed framework overcomes the limitations of face recognition systems as well as fingerprint verification systems. The gray-level co-occurrence matrix and the minutiae extraction are used to represent the features of face and fingerprint image respectively. This framework uses the correlation coefficient as a similarity measure to retrieve the closest face and the corresponding fingerprint images with a query image. Experimental results performed on a given database of face and fingerprint images show that the proposed framework improved greatly the security and recognition rate. General Terms Multimodal biometric, Personal identification, Fingerprint verification, Face recognition.

Feature-level fusion of fingerprint and finger-vein for personal identification

Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion.