Finger Knuckle Print Research Articles

Personal recognition using finger knuckle shape oriented features and texture analysis

Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

Performance Evaluation of Distance Measurement in Biometric Finger Knuckle Print Recognition

Performance Evaluation of Distance Measurement in Biometric Finger Knuckle Print Recognition

Optimized projections for nonnegative linear reconstruction classification

Nonnegative linear reconstruction measure can produce the sparse nonnegative representation coefficients which are concentrated on the similar training samples. The samples with the same class-label are generally more similar than those with different class-labels. Based on this, we develop a nonnegative linear reconstruction based classifiers (NLRC). To enhance the classification performance, we design a feature extractor based on the same decisional rule of NLRC, such that the extracted features are most suitable to NLRC. The proposed feature extractor, nonnegative linear reconstruction projection (NLRP), and NLRC share a common decisional rule, and thus the features extracted by NLRP fit NLRC well in theory. The feasibility and effectiveness of the proposed recognition system are verified on the Yale face database, the AR face database the PolyU finger knuckle print database, and the palmprint database with promising results.

Finger Knuckle Identification using RLF and Dynamic Time Warping

Texture pattern of finger back surface is highly unique consisting of creases and lines which can be used for biometric authentication system. Use of Finger Knuckle Print (FKP) for person identification has been attracted attention of researchers in last few years .Finger Knuckle Print is becoming emerging biometric identifier. In this paper, we present a finger knuckle identification method that uses Dynamic programming (DP) for the alignment of Radon Like Features. The key idea is to use dynamic time warping (DTW) to match Radon Like features of two knuckle images. Experiment is carried out using IIT Delhi finger knuckle database version 1.0. Knuckle features are extracted using the Radon Like Feature technique is classified using DTW for the identification of finger knuckle print. Result obtained using RLF and DTW is promising as compared to subspace and Gabor filtering methods.

Finger Knuckle Print as Unimodal Fuzzy Vault Implementation

A biometric template is a digital reference of distinct characteristics that are extracted from a biometric sample. The feature set obtained during enrollment is stored in the system database as a template.Disparate passwords, when biometric templates are compromised, it is not feasible for a valid user to rescind his biometric identifiers and change to another set of uncompromised identifiers. Due to this irrevocable nature of biometric data, attacks against the stored templates constitute a major security and privacy peril in a biometric system. The primary aim of this paper is to propose a novel technique for security of templates by using fuzzy vault implementation. The first proposed approach is used to extract the feature point from finger knuckle print (FKP). This approach uses an image processing techniques to extract the biometric feature from the user's finger knuckle print.The second approach is one of unimodal fuzzy vault implementation. Fuzzy vault is a cryptographic framework that binds the biometric template with a uniformly random key to build a secure sketch of the template. Only the secure sketch is stored and if the original template is uniformly random, it is not feasible to retrieve either the template or the key without any knowledge of the user's biometric data.The performances of the proposed approaches are evaluated using the experimental observation. The simplicity and efficiency of the proposed methods make it readily applicable in various security fields. The performance of the proposed fuzzy vault implementation has been evaluated for Hong Kong polytechnic university FKP database. In this fuzzy vault implementation for biometric evaluation the performance metrics used are Failure-to-Capture Rate (FTCR), Genuine Accept Rate (GAR) and False Accept Rate (FAR)Performance analysis is carried out in two phases. In the initial phase, the secret key size is varied and the results are obtained. In phase two, the effect of adding salt and pepper noise is considered. Noise is added to analyze the strength of the fuzzy vault implementation.

Finger multibiometric cryptosystem based on score-level fusion

As a very promising approach, multibiometrics has been extensively studied to overcome the limitations of unibiometrics for high recognition performance in recent years. However, multibiometric systems always store multiple biometric templates for each user, which causes significant risks to user privacy and system security. In this paper, we address two critical issues in the design of finger multibiometric system, i.e., fusion methodology and template security. First, the score-level fusion framework using Fuzzy Commitment Scheme is proposed to protect multiple finger biometric templates of fingerprint, finger vein, finger knuckle print and finger shape traits. Second, the security of the proposed multibiometric cryptosystem is analysed on the basis of the correcting capacity of Error Correcting Code. The experimental results and security evaluation are conducted on a merged finger multimodal biometrics database. The comparative experiments show that the proposed finger multibiometric cryptosystem outperforms the unibiometric counterparts in terms of verification performance and template security.

Finger knuckle biometrics – A review

Biometric-based personal authentication is receiving a widespread interest in the area of research due to its high applicability in a wide range of security applications. Among these, hand-based biometric systems are considered to be more successful in terms of accuracy and computational complexity. In hand-based biometrics, finger knuckle surface is considered as one of the emerging potential biometric traits for personal authentication. This is due to its stable and unique inherent patterns present in the outer surface of the finger back knuckle region. Further, this finger knuckle has a high potentiality towards discriminating individuals with high accuracy. In this paper, we present a review of various system models that are implemented for personal authentication using finger knuckle biometrics. Furthermore, the challenges that could arise during the implementation of the large scale real time biometric system with finger knuckle print are explored.

FINGER KNUCKLE PRINT BASED VERIFICATION USING MINIMUM AVERAGE CORRELATION ENERGY FILTER

Biometric-based technologies are being widely used for personal authentication and identification in access control and e-commerce applications. Finger knuckle print, a new hand based biometric trait has recently been utilized in verification and identification based systems. A finger knuckle print possesses a unique and highly distinctive pattern. In this paper, the high discrimination capability of correlation filters were employed for a finger knuckle print based recognition system. The correlation filters have important characteristics like shift invariance and distortion tolerance. A minimum average correlation energy correlation filter was designed for finger knuckle print verification. The performance of the designed filter was evaluated by calculating the peak to side lobe ratio, the false acceptance ratio, the false rejection ratio and the equal error rate. The computational experiments were done on a Matlab platform on the Poly U finger knuckle print database.

Designing an accurate hand biometric based authentication system fusing finger knuckleprint and palmprint

This paper proposes an accurate and efficient multi-modal authentication system that makes use of palm and knuckleprint samples. Biometric images are transformed using the proposed sign of local gradient (SLG). Corner features are extracted from vcode and hcode and are tracked using geometrically and statistically constrained Lucas and Kanade tracking algorithm. The proposed highly uncorrelated features (HUF) measure is used to match two query images. The proposed system is tested on publicly available PolyU and CASIA palmprint databases along with PolyU Knuckleprint database. Several sets of chimeric bi-modal as well as multimodal databases are created in order to test the proposed system. Experimental results reveal that the proposed multi-modal system achieves CRR of 100% with an EER as low as 0.01% over all created chimeric multimodal datasets.

Multimodal biometric authentication based on score level fusion of finger biometrics

In this article, we propose a novel finger multimodal biometric authentication that combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. The proposed multimodal biometrics provides score-level fusion approach based on triangular norm with four finger biometric traits, instead of two or three ones combined in the previous approaches. The experimental evaluations and analysis are conducted on a merged multimodal biometrics database. The results show that the proposed score-level fusion approach using triangular norm obtains a larger distance between genuine and imposter score distribution as well as achieves lower error rates. Moreover, the comparison results suggest that the proposed score level fusion of finger biometrics using triangular norm outperforms the state-of-the-art approaches.

Discriminative common vector based finger knuckle recognition

The main issue in personal authentication systems for military, security, industrial and social applications is accuracy. This paper presents a finger knuckle print (FKP) recognition approach to identity authentication. It applies a discriminative common vectors (DCV) based method to obtain the unique feature vectors, called discriminative common vectors, and the Euclidean distance as matching strategy to achieve the identification and verification tasks. The recognition process can be divided into the following phases: capturing the image; pre-processing; extracting the discriminative common vectors; matching and, finally, making a decision. In order to test and evaluate the proposed approach both the most representative FKP public databases and an established non-uniform FKP database were used. Experiments with these databases confirm that the DCV-based FKP recognition method achieves the authentication tasks effectively. The results showed the performance of the system in terms of the recognition rate had 100% accuracy for both training data and unseen test data.

Finger Knuckle Print Identification using Gabor Features

One of the current trends in biometric human identification is the development of new emerging modalities. Knuckle biometrics is one of such promising modalities. Texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. This paper presents feature based identification methods for an emerging biometric identifier called FingerKnuckle-Print (FKP). Techniques employed for feature based approach is Gabor filter method .In applications of computer vision and image analysis, Gabor filters have maintained their popularity in feature extraction for almost three decades. In the proposed work experiment is carried out to identify finger knuckle images of more than 100 persons. Compared with the other existing finger back surface based biometric system, the proposed FKP system achieves much higher recognition rate.

Finger Multi-biometric Cryptosystem using Feature-Level Fusion

In this paper, we propose a new finger multi-biometric cryptosystem using feature-level fusion to simultaneously protect multiple templates of fingerprint, finger vein, finger knuckle print and finger shape traits as a single secure sketch. We theoretically analyze the featurelevel fusion for finger multi-biometric cryptosystem with respect to their impact on security and recognition accuracy. The comparative experimental results ascertain that finger multibiometric cryptosystem outperforms the uni-biometric counterparts in terms of verification performance and template security.

FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING SDOST

Finger knuckle-print biometric system has widely us ed in modern e-world. The region of interest is needed as the key for the feature extraction in a g ood biometric system. The symmetric discrete orthonormal stockwell transform provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture feature s in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. Thi s motivates us to propose a new local and global feature extractor. For the finger knuckle-print, th e local and global features are critical for an ima ge observation and recognition. For the finger knuckle -print, the local and global information are critic al for an image observation and recognition. The local features of an enhanced finger knuckle-print image are extracted using symmetric discrete orthon ormal stockwell transform. The Fourier transform of an image is obtained by increasing the scale of symmetric discrete orthonormal stockwell transform to infinity. The Fourier transform coefficients ext racted from the finger knuckle-print image is considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The gl obal feature is exploited to refine the arrangement of finger knuckle-print images in matching. The pro posed scheme makes use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance o f two finger knuckle-print images. The investigational results indicate that the proposed work outperforms an existing works with an equal error rate of 0.0045 and 100% correct recognition r ate on the finger knuckle-print database.

A hierarchical classification method for finger knuckle print recognition

Abstract Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

Finger multibiometric cryptosystems: fusion strategy and template security

We address two critical issues in the design of a finger multibiometric system, i.e., fusion strategy and template security. First, three fusion strategies (feature-level, score-level, and decision-level fusions) with the corresponding template protection technique are proposed as the finger multibiometric cryptosystems to protect multiple finger biometric templates of fingerprint, finger vein, finger knuckle print, and finger shape modalities. Second, we theoretically analyze different fusion strategies for finger multibiometric cryptosystems with respect to their impact on security and recognition accuracy. Finally, the performance of finger multibiometric cryptosystems at different fusion levels is investigated on a merged finger multimodal biometric database. The comparative results suggest that the proposed finger multibiometric cryptosystem at feature-level fusion outperforms other approaches in terms of verification performance and template security.

Integration of multiple orientation and texture information for finger-knuckle-print verification

The Competitive Coding (CompCode) scheme, which extracts and codes the local dominant orientation as features, has been widely used in finger knuckle print (FKP) verification. However, CompCode may lose some valuable information such as multiple orientation and texture of the FKP image. To remedy this drawback, a novel multiple orientation and texture information integration scheme is proposed in this paper. As compared with CompCode, the proposed scheme not only considers more orientations, but also introduces a multilevel image thresholding scheme to perform orientation coding on each Gabor filtering response. For texture features extraction, LBP maps are first obtained by performing Local Binary Pattern (LBP) operator on each Gabor filtering response, and then a similar coding scheme is applied on these LBP maps. Finally, multiple orientation and texture features are integrated via score level fusion to further improve FKP verification accuracy. Extensive experiments conducted on the PolyU FKP database show the effectiveness of the proposed scheme.

Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics

Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Correlation Analysis (MCCA). How to extend them to fuse multiple feature sets is a significant problem in general. In this paper, we propose a novel multimodal finger biometric method, which provides feature fusion approach called linear discriminant multi-set canonical correlation analysis (LDMCCA). It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. Compared with CCA and MCCA, LDMCCA contains the class information of the training samples and represents the fused features more efficiently and discriminatively in few dimensions. The experimental results on a merged multimodal finger biometric database show that LDMCCA is beneficial to fuse multiple features as well as achieves lower error rates than the existing approaches.

Reconstruction Based Finger-Knuckle-Print Verification With Score Level Adaptive Binary Fusion

Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentication with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.

Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern

Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build the feature vector of the FKP ROI image. In the match stage, histogram intersection distance is applied as the similarity measurement between sample and template. Experimental results conducted on a database of 165 persons (4 fingers per person) show that the proposed method is effective.