Palmprint Authentication System Research Articles

Palmprint authentication using symbolic aggregate approximation features with improved enhancement techniques

The palmprint enhancement is a pre-processing stage of palmprint authentication. The accuracy of the recognition rate can be improved by incorporating effective palmprint enhancement methods. So far, only a little work has been done in palmprint enhancement. Little attention is given to incorporate the enhancement techniques in palmprint authentication system to achieve performance improvement. This work presents robust enhancement methods which provide better performance and accuracy. Palmprint enhancement using curvelet and Recursive Histogram Equalization (RHE) overcomes the drawbacks of the existing systems and makes palmprint recognition simpler and more accurate with enhanced palm image as input. Palmprint recognition system uses the Symbolic Aggregate Approximation (SAX) features from the enhanced palmprint image. The recognition rate is optimum when both curvelet and RHE methods are used for enhancement of palmprint. Here we compared different types of enhancement methods and we have obtained maximum recognition rate with a combination of recursive histogram equalization and curvelet transform. The performance of the palmprint authentication is measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). The comparison of recognition rate with enhancement is done using minimum distance classifier, Support Vector Machine, Random Forest and Bayesian. Random Forest classifier provides better results.

A fuzzy connective score fusion technique for 2D and 3D palmprint authentication system

A fuzzy connective score fusion technique for 2D and 3D palmprint authentication system

Research and Development of Palmprint Authentication System Based on Android Smartphones

The security problems of online transactions by smartphones reveal extreme demand for reliable identity authentication systems. With a lower risk of forgery, richer texture, and more comfortable acquisition mode, compared with face, fingerprint, and iris, palmprint is rarely adopted for identity authentication. In this paper, we develop an effective and full-function palmprint authentication system regarding the application on an Android smartphone, which bridges the algorithmic study and application of palmprint authentication. In more detail, an overall system framework is designed with complete functions, including palmprint acquisition, key points location, ROI segmentation, feature extraction, and feature coding. Basically, we develop a palmprint authentication system having user-friendly interfaces and good compatibility with the Android smartphone. Particularly, on the one hand, to guarantee the effectiveness and efficiency of the system, we exploit the practical Log-Gabor filter for feature extraction and discuss the impact of filtering direction, downsampling ratio, and discriminative feature coding to propose an improved algorithm. On the other hand, after exploring the hardware components of the smartphone and the technical development of the Android system, we provide an open technology to extend the biometric methods to real-world applications. On the public PolyU databases, simulation results suggest that the improved algorithm outperforms the original one with a promising accuracy of 100% and a good speed of 0.041 seconds. In real-world authentication, the developed system achieves an accuracy of 98.40% and a speed of 0.051 seconds. All the results verify the accuracy and timeliness of the developed system.

An approach to improve Palmprint Recognition Accuracy by Using Different Region of Interest Methods with Local Binary Pattern Techniques

Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy. Keywords: Analysis, Competitive Hand Valley Detection (CHVD), Euclidean Distance, Local Binary Pattern (LBP), Palmprint Recognition

An approach to improve Palmprint Recognition Accuracy by Using Different Region of Interest Methods with Local Binary Pattern Techniques

Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy.

Multi-algorithmic Palmprint Authentication System Based on Score Level Fusion

Abstract Fusion of multiple algorithms utilizes as much information as possible from each algorithm for enhancing the performance of the biometric authentication system. It is a big challenge to formulate a single algorithm for any biometric authentication system to addresses the problem of illumination, orientations and pose variations. The palmprint features are extracted using two feature extraction algorithms namely contourlet transform with principal component analysis and dual-tree complex wavelet transform. The match scores obtained from matching modules were normalized using z-score and fused with different score level fusion schemes namely sum-rule,weighted sum-rule, and Support Vector Machine (SVM) fusion, respectively. The experimental results show that SVM score level fusion lead to an increased performance for the proposed multi-algorithmic palmprint authentication system with genuine acceptance rate of 98% for 0.1% false acceptance rate, and equal error rate of 1.5% compared to weighted sum-rule and sum-rule fusion schemes.

Developing a contactless palmprint authentication system by introducing a novel ROI extraction method

In this paper, we propose a novel contactless palmprint authentication system where the system uses a CCD camera to capture the user's hand at a distance without any restrictions and touching the device. Furthermore, a novel and high performance region of interest (ROI) extraction method which makes use of nonlinear regression and palm model to extract the ROIs with high success is proposed. Comparative results indicate that the proposed ROI extraction method gives superior performance as compared to the previously proposed point-based approaches. To show the performance of the proposed system, a novel contactless database has also been created. This database includes images captured from the users who present their hands with various hand positions and orientations in cluttered backgrounds. Furthermore, experiments show that the proposed system has achieved a recognition rate of 99.488% and equal error rate of 0.277% on the contactless database of 145 people containing 1752 hand images.

Accuracy Improvement in Palmprint Authentication System

Biometric authentication has been emerged as a reliable means to control a person’s access to physical and virtual places. Despite the various efforts made on biometrics, accuracy of the authentication/identification is the main concern and it has to be completely investigated. The paper presents critical analysis of the matching score values in such a manner that system accuracy is increased. Min Max Threshold Range (MMTR) technique is proposed that provides two levels of authentication and increase in accuracy is observed. The methodology of increase in accuracy is observed on various feature extraction methods.

A secure palmprint authentication system using chaotic mixing and watermarking

In recent years, there has been a significant increase in the use of biometrics for user authentication. The wide spread use of biometrics in real life applications for user authentication raised the security and privacy concerns of the biometric data. In this paper, a biometric image protection scheme exploiting the properties of chaotic mixing and watermarking technique is proposed. We also proposed an efficient technique for biometric template protection using a random vector. Detailed discussions on the security of the proposed techniques are given. The effectiveness of the proposed method is investigated with application to palmprint authentication. The experimental results evaluated on PolyU and IITD database shows that the proposed system achieves better recognition accuracy and lower error rates while providing protection to the biometric data.

Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST

Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.

A remote cancelable palmprint authentication protocol based on multi‐directional two‐dimensional PalmPhasor‐fusion

ABSTRACTBiometric template security and privacy issues are critical in biometric authentication systems and require special attention. However, remote biometric authentication systems demand wider array of measures for maximum protection. This paper proposes a remote cancelable palmprint authentication protocol based on multi‐directional two‐dimensional PalmPhasor‐fusion. The main contribution is three‐fold. First, with a transposition direction selection mechanism, multi‐directional two‐dimensional PalmPhasor (MTDPP) improves the accuracy performance of two‐dimensional PalmPhasor. Second, we provide the theoretical analysis of the effect of transposition on the accuracy performance of two‐dimensional PalmPhasor, and hence establish an effective transposition direction range for the proposed MTDPP. Third, according to our analysis, the existing remote palmprint authentication system does not satisfy non‐invertibility criterion of secure template protection and is vulnerable to interception. Besides, secret message embedding as a countermeasure for database attacks deteriorates accuracy performance and causes inconvenience in updating authenticator. The proposed protocol uses multi‐directional two‐dimensional PalmPhasor‐fusion, one‐time random number encrypted with asymmetric cryptography and encrypted hash codes of MTDPP to address the problems. Copyright © 2013 John Wiley & Sons, Ltd.

Peg Free Multi-Dimensional Palmprint Feature Extraction Using Hybrid Level Fusion Strategy

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palm print and geometrical features of the hand. The geometrical features are obtained from the binaries images by using Otsu’s threshold technique. Multimodal (2-D and 3D) palm print and hand geometry features, which are simultaneously extracted from the user’s textured 2-D and 3-D hand, are used for matching. Individual matching scores are then combined using a new Hybrid level fusion strategy. The objective of this work is to improve accuracy and robustness of existing palm print authentication systems using combination of the 2D and 3D palm print features. A peg-free system is composed by a pc and camera. The users put the hand in free space in front of the camera. The hand is illuminated by an infra- red light to solve segmentation problems in a real environment. The surface curvature feature based method is investigated for Gabor feature based competitive coding scheme is used for 2D representation. The database of 120 subjects achieved significant improvement in performance with the integration of 2D and 3D palm print and hand geometry features. Such as those mounted on a laptop, mobile device, and web camera or those for surveillance, can dramatically increase the applicability of such a system. However, the performance of existing techniques for palm print authentication falls considerably, when the camera is not aligned with the surface of the palm. The experimental results also suggest that the Hybrid level fusion approach employed in this work helps to achieve the performance improvement of 70% (in terms of EER) over the case when matching scores are combined using the Dynamic fusion approach.

Automatic Palmprint Identification based on High Order Zernike Moment

Problem statement: Hand geometry contains relatively invariant features of an individual. Palmprint recognition is an efficient biometric solution for authentication system. The existence of several hand-based authentication commercial systems indicates the effectiveness of this type of biometric. Approach: We proposed a palmprint verification system using high order Zernike moment that was robust to rotation, translation and occlusion. Zernike moment was an efficient algorithm for representing the shape features of an image. The design consists of feature extraction and matching of image using high order Zernike moment. Zernike moments at high orders was calculated from the image and the image was classified using K-Nearest Neighborhood (KNN). The reason for using Zernike moment was that it was the best algorithm due to its orthogonality and rotation invariance property. Results and Conclusion: Computational cost can be reduced by detecting the common term of Zernike moment. Experiments and classifications have been performed using Hong Kong PolyU palm print database with 125 individuals’ left hand palm images; every person has 5 samples, totaling up to 625. We then get every person’s palm images as a template (totaling 125). The remaining 500 are used as the training samples. The proposed palmprint authentication system achieves a recognition accuracy of 98% and interesting working point with False Acceptance Rate (FAR) of = 1.062% and False Rejection Rate (FRR) of = 0%. Experimental evaluation demonstrates the efficient recognition performance of the proposed algorithm compared with conventional palmprint recognition algorithms.

PERSONAL AUTHENTICATION USING PALMPRINT WITH SOBEL CODE, CANNY EDGE AND PHASE CONGRUENCY FEATURE EXTRACTION METHOD

Palmprint recognition refers to recognizing a person on the basis of palmprint features. In this paper, we have proposed a palmprint based biometric authentication method with improvement in accuracy, so as to make it a real time palmprint authentication system. Several edge detection methods, Directional operator, Wavelet transform, Fourier transform etc. are available to extract line feature from the palmprint. In this paper, Sobel Code operators, Canny edge and Phase Congruency methods are applied to the palmprint image to extract palmprint features. The extracted Palmprint features are stored in Palmprint feature vector. The corresponding feature vectors are matched using sliding window with Hamming Distance similarity measurement method. In this paper, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by reducing the False Acceptance Rate (FAR). The person authenticated by reference threshold is again verified by second level of authentication using MMTR method. Experimental results indicate that the MMTR method improves the False Acceptance Rate drastically. The accuracy improvement leads to proposed real time authentication system.

FAST PALMPRINT AUTHENTICATION BY SOBEL CODE METHOD

The ideal real time personal authentication system should be fast and accurate to automatically identify a person’s identity. In this paper, we have proposed a palmprint based biometric authentication method with improvement in time and accuracy, so as to make it a real time palmprint authentication system. Several edge detection methods, wavelet transform, phase congruency etc. are available to extract line feature from the palmprint. In this paper, Multi-scale Sobel Code operators of different orientations (0, 45, 90, and 135) are applied to the palmprint to extract Sobel-Palmprint features in different directions. The Sobel-Palmprint features extracted are stored in SobelPalmprint feature vector and matched using sliding window with Hamming Distance similarity measurement method. The sliding window method is accurate but time taking process. In this paper, we have improved the sliding window method so that the matching time reduces. It is observed that there is 39.36% improvement in matching time. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by reducing the False Acceptance Rate (FAR). Experimental results indicate that the MMTR method improves the False Acceptance Rate drastically and improvement in sliding window method reduces the comparison time. The accuracy improvement and matching time improvement leads to proposed real time authentication system.

New Palm Print Authentication System by Using Wavelet Based Method

New Palm Print Authentication System by Using Wavelet Based Method

A novel chaotic stream cipher and its application to palmprint template protection

Based on a coupled nonlinear dynamic filter (NDF), a novel chaotic stream cipher is presented in this paper and employed to protect palmprint templates. The chaotic pseudorandom bit generator (PRBG) based on a coupled NDF, which is constructed in an inverse flow, can generate multiple bits at one iteration and satisfy the security requirement of cipher design. Then, the stream cipher is employed to generate cancelable competitive code palmprint biometrics for template protection. The proposed cancelable palmprint authentication system depends on two factors: the palmprint biometric and the password/token. Therefore, the system provides high-confidence and also protects the user's privacy. The experimental results of verification on the Hong Kong PolyU Palmprint Database show that the proposed approach has a large template re-issuance ability and the equal error rate can achieve 0.02%. The performance of the palmprint template protection scheme proves the good practicability and security of the proposed stream cipher.

An Online System of Multispectral Palmprint Verification

Palmprint is a unique and reliable biometric characteristic with high usability. With the increasing demand of highly accurate and robust palmprint authentication system, multispectral imaging has been employed to acquire more discriminative information and increase the antispoof capability of palmprint. This paper presents an online multispectral palmprint system that could meet the requirement of real-time application. A data acquisition device is designed to capture the palmprint images under Blue, Green, Red, and near-infrared (NIR) illuminations in less than 1 s. A large multispectral palmprint database is then established to investigate the recognition performance of each spectral band. Our experimental results show that the red channel achieves the best result, whereas the Blue and Green channels have comparable performance but are slightly inferior to the NIR channel. After analyzing the extracted features from different bands, we propose a score level fusion scheme to integrate the multispectral information. The palmprint verification experiments demonstrated the superiority of multispectral fusion to each single spectrum, which results in both higher verification accuracy and antispoofing capability.

Min Max Threshold Range (MMTR) based approach in palmprint authentication by sobel code method

Palmprint recognition is an effective biometric authentication method to automatically identify a person’s identity. Palmprint is rich in features like geometry features, line features, datum points, delta features and minutiae features. Several edge detection methods are available to extract line feature from the palmprint. In this paper, the hand image is pre-processed to get the desired Region of Interest (ROI)/palmprint. Multiscale Sobel Code operators of different orientations (0°, 45°, 90°, and 135°) are applied to the palmprint to extract Sobel-Palmprint features in different directions. The Sobel-Palmprint features extracted are stored in Sobel-Palmprint feature vector and matched using Hamming Distance similarity measurement method. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by matching a person with multiple threshold values. In this technique, firstly the person is authenticated at global level using Reference threshold. Secondly, the person is authenticated at local level using range of Minimum and Maximum thresholds defined for a person. Generally, personal authentication is done using reference threshold but there are chances of false acceptance. So, by using the Minimum and Maximum Thresholds range of false accepted persons at personal level, a person is identified to be false accepted or genuinely accepted. MMTR is an effective technique to increase the accuracy of the palmprint authentication system by reducing the False Acceptance Rate (FAR). Experimental results indicate that the proposed method improves the False Acceptance Rate drastically.

Palmprint verification using binary orientation co-occurrence vector

The development of accurate and robust palmprint verification algorithms is a critical issue in automatic palmprint authentication systems. Among various palmprint verification approaches, the orientation based coding methods, such as competitive code (CompCode), palmprint orientation code (POC) and robust line orientation code (RLOC), are state-of-the-art ones. They extract and code the locally dominant orientation as features and could match the input palmprint in real-time and with high accuracy. However, using only one dominant orientation to represent a local region may lose some valuable information because there are cross lines in the palmprint. In this paper, we propose a novel feature extraction algorithm, namely binary orientation co-occurrence vector (BOCV), to represent multiple orientations for a local region. The BOCV can better describe the local orientation features and it is more robust to image rotation. Our experimental results on the public palmprint database show that the proposed BOCV outperforms the CompCode, POC and RLOC by reducing the equal error rate (EER) significantly.