Palmprint Verification Research Articles

Palmprint Verification for Images Captured in Peg-Less Scenarios

This paper presents a reliable and robust palmprint verification approach using palmprint feature point number (FPN). The feature verified by support vector machine (SVM). It has the advantages of capturing palm images in peg-less scenarios and by a low cost and low-resolution (100dpi) digital scanner. The low-resolution images lead a less database size. There are 4800 palmprint images were collected from 160 persons to verify the validity of the proposed approach and the results are satisfactory with 98.30% classification correct rate (CCR). Experimental results demonstrate that the proposed approach is feasible and effective in palmprint verification. Our findings will help to extend palmprint verification technologies to security access control systems.

Palmprint Verification by Scene Matching Approaches

This paper presents two novel approaches for palmprint verification by scene match- ing. In a sense, the process of palmprint verification can be regarded as matching one palmprint template with another palmprint sample taken under conditions of different illumination, sensor pose geometry, or types of sensor, and making a decision of whether they belong to the same person or not. Enlightened by the idea of scene matching algorithms, two efficient approaches by scene matching are presented for palmprint verification. A sub-image from the user’s training palmprint is intentionally extracted, and stored as a template. In the verification stage, the sample image is directly matched with the template without the step of ROI (Region of Interest) extraction. Whether the sample image and the template are from the same person or not is decided by their matching scores. Experimental evaluation results on two databases clearly demonstrate the effectiveness of the proposed approaches.

Principal Line-Based Alignment Refinement for Palmprint Recognition

Image alignment is an important step in various biometric authentication applications such as palmprint recognition. Most of the existing palmprint alignment methods make use of some key points between fingers or in palm boundary to establish the local coordinate system for region of interest (ROI) extraction. The ROI is consequently used for feature extraction and matching. Such alignment methods usually yield a coarse alignment of the palmprint images, while many missed and false matches are actually caused by inaccurate image alignments. To improve the palmprint verification accuracy, in this paper, we present an efficient palmprint alignment refinement method. After extracting the principal lines from the palmprint image, we apply the iterative closest point method to them to estimate the translation and rotation parameters between two images. The estimated parameters are then used to refine the alignment of palmprint feature maps for a more accurate palmprint matching. The experimental results show that the proposed method greatly improves the palmprint recognition accuracy and it works in real time.

Fragile Bits in Palmprint Recognition

Recent years have witnessed a growing interest in developing automatic palmprint recognition methods. Among them, coding-based ones, representing the texture of a palmprint using a binary code, are most prevalent and successful. We find that not all bits in a code map generated by a specific coding scheme are equally consistent. A bit is deemed fragile if its value changes across code maps created from different images of the same palmprint. In this paper, we first analyze the fragile bits phenomenon in a state-of-the-art palmprint coding scheme, namely, binary orientation co-occurrence vector (BOCV). Then, based on our analysis, we extend BOCV to E-BOCV by incorporating fragile bits information in appropriate ways. Experiments conducted on the benchmark dataset demonstrate that E-BOCV can achieve the highest verification accuracy among all the state-of-the-art palmprint verification methods evaluated. To our knowledge, this is the first work investigating the fragile bits of coding-based palmprint recognition approaches.

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.

A Novel Design for a Palm Prints Enabled Biometric System

In this paper we are providing an approach for authentication using palm prints. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant and easy to acquire. Palm print is one of the relatively new physiological biometrics due to its stable and unique characteristics. In this paper, a combination of hand geometry and palmprint verification system is being developed. This system uses a scanner as sole sensor to obtain the hands images. First, the hand geometry verification system performs the feature extraction to obtain the geometrical information of the fingers and palm. Second, the region of interest (ROI) is detected and cropped by palmprint verification system. This ROI acts as the base for palmprint feature extraction by using Linear Discriminant Analysis (LDA). Lastly, the matching scores of the two individual classifiers is fused by several fusion algorithms namely sum rule, weighted sum rule and Support Vector Machine (SVM). The results of the fusion algorithms are being compared with the outcomes of the individual palm and hand geometry classifiers. We are able to show that fusion using SVM with Radial Basis Function (RBF) kernel has outperformed other combined and individual classifiers.

Palmprint verification based on 2D – Gabor wavelet and pulse-coupled neural network

To alleviate the limitation that the recent texture based algorithms for palmprint recognition yield unsatisfactory robustness to the variations of orientation, position and illumination in capturing palmprint images, this paper describes a novel texture based algorithm for palmprint recognition combining 2D Gabor wavelets and pulse coupled neural network (PCNN). In the proposed algorithm, palmprint images are decomposed by 2D Gabor wavelets, and then PCNN is employed to imitate the creatural vision perceptive process and decompose each Gabor subband into a series of binary images. Entropies for these binary images are calculated and regarded as features. A support vector machine-based classifier is employed to implement classification. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages and relatively high robustness to the variations of orientation, position and illumination compared with the recent texture based approaches.

Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification

Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification

Palmprint Verification Using Multi-scale Gradient Orientation Maps

This paper proposes a new approach to palmprint verification based on the gradient, in which a palm image is considered to be a three-dimensional terrain. Principal lines and wrinkles make deep and shallow valleys on a palm landscape. Then the steepest slope direction in each local area is first computed using the Kirsch operator, after which an orientation map is created that represents the dominant slope direction of each pixel. In this study, three orientation maps were made with different scales to represent local and global gradient information. Next, feature matching based on pixel-unit comparison was performed. The experimental results showed that the proposed method is superior to several state-of-the-art methods. In addition, the verification could be greatly improved by fusing orientation maps with different scales.

Robust Algorithms for a Multimodal Biometric System Using Palmprint and Speech

In this paper, we propose a person verification scheme using a novel combination of palmprint and speech. The crucial aspect of biometric based verification lies in its use of features in verification. Thus, in this paper, we propose two novel feature extraction methods for palmprint verification. The proposed methods are based on the Gaussian mixture model followed by subspace based approaches such as ICA I and ICA II, called independent component analysis I mixture model (ICA I MM) and independent component analysis II mixture model (ICA II MM) for palmprint verification. The speech verification system uses sphericity based measurement for person verification. The proposed multimodal biometric system uses a fairly large database of 150 users. The robustness of the proposed methods is validated by introducing six different types of noises on palmprint images and Gaussian white noise with two different signal-to-noise ratios on speech samples. Then, fusion of palmprint and speech are carried out at match score level using the weighted SUM rule for both clean and noisy conditions. Finally, we compare the results of combined biometric with the results of individual biometric and also the results of the proposed methods against conventional (without mixture model) subspace methods.

AN EFFICIENT WAVELET-BASED PALMPRINT VERIFICATION APPROACH

This paper proposes a wavelet-based palmprint verification approach which is efficient in terms of accuracy and speed. The prominent wavelet domain features such as subband energy distribution, histogram, and co-occurrence features fail to characterize the palmprints sufficiently due to coefficient perturbations caused by translational and/or rotational variations in palmprints. In this work, firstly, a novel approach, termed as adaptive tessellation of subbands, is proposed to effectively capture the spatially localized energy distribution based on the spread of principal lines. Secondly, a set of discriminating features, termed as high scale codes (HSCODEs), and a translation and rotation invariant matching technique are proposed. HSCODEs effectively characterize the palmprints by capturing the spatial patterns corresponding to the low frequency components. Energy features and selected HSCODEs are fused at score and decision levels. Particularly, score level fusion enhances the verification accuracy significantly. Effectiveness of the proposed approach is examined on PolyU-ONLINE-Palmprint-II (PolyU) database. The experimental results show an overall equal error rate (EER) of 0.22%, which is better than the existing wavelet-based palmprint recognition systems and comparable to the computationally complex state-of-the-art approaches. The speed of the approach is high as all the features are extracted from the same wavelet decomposition of palmprint. Further, it is shown that the proposed feature extraction technique can be extended for speech signals as well and such features can be fused with palmprint features for accuracy enhancement.

국부 이진패턴 히스토그램을 이용한 장문인식

This paper proposes an efficient method for verifying palmprint which is captured at the natural interface without any physical restriction. The location and orientation of the region of interest (ROI) in palm images are variously appeared due to the translation and rotation of hand. Therefore, it is necessary to extract the ROI stably for palmprint recognition. This paper presents a method that can extract the ROI, which is based on the reference points that are located at the center of the crotch segments between index finger and middle finger and between ring finger and little finger. It also proposes a palmprint recognition method using the histogram of local binary patterns (LBP). Experiments for evaluating the performance of the proposed method were performed on 1,597 palmprint images acquired from 100 different persons. The experimental results showed that ROI was correctly extracted at the rate of 99.5% and the equal error rate (EER) and the decidability index d` indicating the performance of palmprint verification were 0.136 and 3.539, respectively. These results demonstrate that the proposed method is robust to the variations of the translation and rotation of hand.

A SCANNER BASED PALMPRINT VERIFICATION SYSTEM FOR CIVIL APPLICATION

The concept of capturing a palmprint using a scanner is not new, but the current palmprint capture devices based on a scanner have the limitations of low speed and large weight. A new palmprint capture device based on a scanner has been developed, which can overcome these limitations. A scanner based palmprint verification system (PVS) has also been designed using the proposed palmprint capture device. A commercially available mobile internet device (MID) was used to execute the whole program and to store the collected palmprint images. A small palmprint database was built using the developed palmprint capture device and the experimental results on this palmprint database showed that the designed system achieved an acceptable level of performance. The proposed PVS has a stable performance, small size and low price, and can meet the practical needs of civil palmprint applications.

Online joint palmprint and palmvein verification

As a unique and reliable biometric characteristic, palmprint verification has achieved a great success. However, palmprint alone may not be able to meet the increasing demand of highly accurate and robust biometric systems. Recently, palmvein, which refers to the palm feature under near-infrared spectrum, has been attracting much research interest. Since palmprint and palmvein can be captured simultaneously by using specially designed devices, the joint use of palmprint and palmvein features can effectively increase the accuracy, robustness and anti-spoof capability of palm based biometric techniques. This paper presents an online personal verification system by fusing palmprint and palmvein inforA fast palmprint and palmvein recognition systemA fast palmprint and palmvein recognition system quality can vary much, a dynamic fusion scheme which is adaptive to image quality is developed. To increase the anti-spoof capability of the system, a liveness detection method based on the image property is proposed. A comprehensive database of palmprint–palmvein images was established to verify the proposed system, and the experimental results demonstrated that since palmprint and palmvein contain complementary information, much higher accuracy could be achieved by fusing them than using only one of them. In addition, the whole verification procedure can be completed in 1.2 s, which implies that the system can work in real time.

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.

Palmprint authentication using modified legendre moments

Abstract The resolution of the acquired biometric image is considered as one of the primary factors affecting the performance of a biometric authentication system. For low quality images, a powerful feature extraction method is very essential. Orthogonal Legendre moments are used in several pattern recognition and image processing applications for feature extraction. Translation and scale invariant Legendre moments are achieved directly by using the Legendre polynomial. However, this method does not yield a rotational invariant form. In this paper we propose a palmprint verification system in which the 2D Legendre moments are represented as a linear combination of geometric moment invariants. Geometric moments are invariant to translation, non-uniform scaling and rotation. The modified Legendre moments are used for feature extraction and a weighted fusion technique is used to fuse the matching scores of the sub-images. The results obtained using a Baye’s classifier indicate an impressive prediction accuracy of 98%, validating the choice of low order Legendre moment for effective palmprint verification.

A unified distance measurement for orientation coding in palmprint verification

Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences between these two distance measures. In this paper, we propose a unified distance measure, of which both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures.

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.

Robust palmprint verification using 2D and 3D features

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. The objective of our work is to improve accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed multilevel framework for personal authentication efficiently utilizes the robustness (against spoof attacks) of the 3D features and the high discriminating power of the 2D features. The developed system uses an active stereo technique, structured light, to simultaneously capture 3D image or range data and a registered intensity image of the palm. The surface curvature feature based method is investigated for 3D palmprint feature extraction while Gabor feature based competitive coding scheme is used for 2D representation. We comparatively analyze these representations for their individual performance and attempt to achieve performance improvement using the proposed multilevel matcher that utilizes fixed score level combination scheme to integrate information. Our experiments on a database of 108 subjects achieved significant improvement in performance with the integration of 3D features as compared to the case when 2D palmprint features alone are employed. We also present experimental results to demonstrate that the proposed biometric system is extremely difficult to circumvent, as compared to the currently proposed palmprint authentication approaches in the literature.

Phase congruency features for palm-print verification

The existing palm-print verification schemes have demonstrated good verification performance when identity claims have to be verified based on palm-print images of adequate quality (e.g. acquired in controlled illumination conditions, free from distortions caused by the pressure applied to the surface of the scanner etc.). However, most of these schemes struggle with their verification performance when features have to be extracted from palm-print images of a poorer quality. In this study the authors present a novel palm-print feature extraction approach which deals with the presented problem by employing the two-dimensional phase congruency model for line-feature extraction. The proposed approach first computes a set of phase congruency features from a palm-print image and subsequently performs linear discriminant analysis on the computed features to represent them in a more compact manner. The approach was tested on two contrasting databases, namely, on the FE-LUKS and on the PolyU database. Encouraging results were achieved on both databases.