Online palmprint identification

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.     

2D and 3D palmprint fusion and recognition using PCA plus TPTSR method

This paper employs both two-dimensional (2D) and three-dimensional (3D) features of palmprint for recognition. While 2D palmprint image contains plenty of texture information, 3D palmprint image contains the depth information of the palm surface. Using two different features, we can achieve higher recognition accuracy than using only one of them. In addition, we can improve the robustness. To recognize palmprints, we use two-phase test sample representation (TPTSR) which is proved to be successful in face recognition. Before TPTSR, we perform principal component analysis to extract global features from the 2D and 3D palmprint images. We make decision based on the fusion of 2D and 3D features matching scores. We perform experiments on the PolyU 2D + 3D palmprint database which contains 8,000 samples and achieve satisfying recognition performance.     

基于曲面曲率和RLDA的3D掌纹识别方法

提出一种新的3D掌纹识别方法,利用掌纹曲面的3D曲率信息提高当前2D掌纹识别算法的精确度和鲁棒性。首先因曲率是3维物体的重要表征,能够与视点无关地表现曲面的局部形状,不管手掌发生旋转或者平移,曲率信息都是稳定的,因此提取3D掌纹的均值曲面曲率特征来刻画3D掌纹的曲面特征;继而获得3D掌纹映射到2D空间上的灰度图像——均值曲率图像(MCI);然后在获得的MCI上采用正则化的LDA(RLDA)方法来进行二次特征抽取,以消除传统线性判别分析(LDA)应用于识别时存在的小样本问题和优化准则函数并不直接与识别率相关等问题。实验结果表明,所提出的方法相比与传统的LDA、PCA、ICA、LPP等方法有更高的精度和速度。     

Personal authentication using multiple palmprint representation

Although several palmprint representations have been proposed for personal authentication, there is little agreement on which palmprint representation can provide best representation for reliable authentication. In this paper, we characterize user's identity through the simultaneous use of three major palmprint representations and achieve better performance than either one individually. This paper also investigates comparative performance between Gabor, line and appearance based palmprint representations and using their score and decision level fusion. The combination of various representations may not always lead to higher performance as the features from the same image may be correlated. Therefore we also propose product of sum rule which achieves better performance than any other fixed combination rules. Our experimental results on the database of 100 users achieve 34.56% improvement in performance (equal error rate) as compared to the case when features from single palmprint representation are employed. The proposed usage of multiple palmprint representations, especially on the peg-free and non-contact imaging setup, achieves promising results and demonstrates its usefulness.     

A study on vulnerability and presentation attack detection in palmprint verification system

As biometric systems become ubiquitous in the domain of personal authentication, it is of utmost importance that these systems are secured against attacks. Among various types of attacks on biometric systems, the presentation attack, which involves presenting a fake copy (artefact) of the real biometric to the biometric sensor to gain illegitimate access, is the most common one. Despite the serious threat posed by these attacks, not much work has been done to address this vulnerability in palmprint-based biometric systems. This paper demonstrates the vulnerability of a palmprint verification system to presentation attacks and proposes a novel presentation attack detection (PAD) approach to discriminating between real biometric samples and artefacts. The proposed PAD approach is inspired by a work that established relationship between the surface reflectance and a set of statistical features extracted from the image. Specifically, statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients and the grey-level co-occurrence matrix form the original feature set, and CFS-based feature selection approach selects the most discriminating feature subset. A trained binary classifier utilizes the selected feature subset to determine whether the acquired image is of real hand or an artefact. For performance evaluation, an antispoofing database—PALMspoof has been developed. This database comprises left- and right-hand images of 104 subjects, and three kinds of artefacts generated from these images. In addition to PALMspoof database, the biometric system’s vulnerability has been assessed on display and print artefacts generated from two publicly available palmprint datasets. Our experimental results show that 1) the palmprint verification system is highly vulnerable with spoof acceptance of 84.56%; 2) the proposed PAD approach is effective against both print and display attacks, in both same-device and cross-device scenarios; and 3) the proposed approach for PAD provides an average improvement of 12.73 percentage points in classification error rate over local binary pattern (LBP)-based PAD approach.     

改进Contourlet变换的3D掌纹图像识别

提出一种基于改进Contourlet变换的3D掌纹图像识别方法;该方法通过形状指数将3D掌纹图像映射成灰度图像,以克服常用的均值或高斯曲率映射难于精确描述3D掌纹特征的缺点;基于此,将7/5滤波器引入Contourlet变换,并在变换域提取形状指数映射图各方向子带的均值与方差作为掌纹图像的特征信息,从而有效利用了Contourlet变换优越的方向特征表达能力,又可有效消除传统Contourlet变换各子图像存在的相关性;最后采用欧氏距离最近邻分类法,实现了测试图像的分类识别。实验结果表明,针对香港理工大学所提供的三维掌纹数据库,该方法总体识别率较PCA方法提高了2.9%,具有明显的优势。     

Implementation and evaluation of a remote authentication system using touchless palmprint recognition

When a cellular phone is lost or stolen, it may be used improperly or the personal information may be stolen from it by a malicious user. Biometric authentication such as palmprint recognition is the strongest of the personal authentication technologies designed to prevent such misuse. In biometric authentication, when compared with a local authentication model, a remote authentication model has several advantages such as direct authentication and authentication levels. Ito et al. proposed several palmprint recognition schemes using correspondence matching based on the phase-only correlation. However, these schemes require a palmprint image to be captured with the hand touching the dedicated device, while palmprint images must be captured without such physical contact when using cellular phones. Thus, these schemes cannot be applied to cellular phones since there are large positioning gaps and large differences in brightness and distortion between the images. Furthermore, they have not been implemented in cellular phones and their performances have not been evaluated either. In this paper, we adopt a remote authentication model from the two types of biometric authentication incorporating the above advantages and propose a remote system between a cellular phone and an authentication server. We implement the proposed system using two different types of Android terminal as the terminal on the user side. We also show the validity of the proposed system by examining and confirming the accuracy and processing time. We furthermore discuss the problem of an impersonation attack on the proposed system and consider solutions to this problem from the viewpoints of security and usability. Then, we adopt a palmprint recognition scheme as a biometric authentication scheme and, in particular, use a palmprint recognition algorithm that incorporates Yörük et al.’s preprocessing technique to Ito et al.’s and Iitsuka et al.’s schemes.     

Fusion of palm-phalanges print with palmprint and dorsal hand vein

To ensure the high performance of a biometric system, various unimodal systems are combined to evade their constraints to form a multimodal biometric system. Here, a multimodal personal authentication system using palmprint, dorsal hand vein pattern and a novel biometric modality “palm-phalanges print” is presented. Firstly, we have collected a new anterior hand database of 50 individuals with 500 images at the institute referred to as NSIT Palmprint Database 1.0 by using NSIT palmprint device. Then from these anterior hand images, database for palmprint and palm-phalanges is created. In this biometric system, the individuals do not have to undergo the distress of using two different sensors since the palmprint and palm-phalanges print features can be captured from the same image, using NSIT palmprint device, at the same time. For dorsal hand vein, Bosphorus Hand Vein Database is used because of the stability and uniqueness of hand vein patterns. We propose fusion of three different biometric modalities which includes palmprint (PP), palm-phalanges print (PPP) and dorsal hand vein (DHV) and perform score level fusion of PP-PPP, PP-DHV, PPP-DHV and PP-PPP-DHV strategies. Lastly, we use K-nearest neighbor, support vector machine and random forest to validate the matching stage. The results proved the validity of our proposed modality and show that multimodal fusion has an edge over unimodal fusion.     

Hierarchical palmprint identification via multiple feature extraction

Biometric computing offers an effective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.     

Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. It is well recognized that in biometric systems feature extraction and representation are key considerations. Among various feature extraction and representation schemes, coding-based methods are most attractive because they have the merits of high accuracy, robustness, compactness and high matching speed, and thus they have been adopted in many different kinds of biometric systems, such as iris, palmprint, and finger-knuckle-print based ones. However, how to devise a good coding scheme is still an open issue. Recent studies in image processing and applied mathematics have shown that local image features can be well extracted with Riesz transforms in a unified framework. Thus, in this paper we propose to utilize Riesz transforms to encode the local patterns of biometric images. Specifically, two Riesz transforms based coding schemes, namely RCode1 and RCode2, are proposed. They both use 3-bits to represent each code and employ the normalized Hamming distance for matching. RCode1 and RCode2 are thoroughly evaluated and compared with the other 3-bit coding methods on a palmprint database and a finger-knuckle-print database. Experiments show that the proposed methods, especially RCode2, could achieve quite similar verification accuracies with the state-of-the-art method (CompCode) while they need much less time at the feature extraction stage, which renders them better candidates for time critical applications.     

A 3D face and hand biometric system for robust user-friendly authentication

A complete authentication system based on fusion of 3D face and hand biometrics is presented and evaluated in this paper. The system relies on a low cost real-time sensor, which can simultaneously acquire a pair of depth and color images of the scene. By combining 2D and 3D facial and hand geometry features, we are able to provide highly reliable user authentication robust to appearance and environmental variations. The design of the proposed system addresses two basic requirements of biometric technologies: dependable performance under real-world conditions along with user convenience. Experimental evaluation on an extensive database recorded in a real working environment demonstrates the superiority of the proposed multimodal scheme against unimodal classifiers in the presence of numerous appearance and environmental variations, thus making the proposed system an ideal solution for a wide range of real-world applications, from high-security to personalization of services and attendance control.     

Development of Palmprint Verification System Using Biometrics

1 Introduction Reliability in personal authentication is key to the security in any transactional database. Many physiological characteristics of humans i.e., biometrics, are typically time invariant, easy to acquire, and unique for every individual. Biom…     

3D palmprint recognition using shape index representation and fragile bits

Recent years have witnessed a growing interesting in developing automatic palmprint recognition methods. Most of the previous works have concentrated on two dimensional (2D) palmprint recognition in the past decade. However, the shape information is lost in 2D plamprint images. What’s more, 2D plamprint recognition is not robust enough in practice since its data could be easily counterfeited or contaminated by noise. Consequently, three dimensional (3D) palmprint recognition is treated as an important alternative road to both enhance the performance and robustness of current available palmprint recognition systems. In this paper, we first explore geometrical information of 3D palmprint data by employing shape index formulation, from which Gabor wavelet features are then extracted. Furthermore, we first discover that by incorporating fragile bits information, the performance of coding strategy related 3D recognition method can be further improved. Experiments conducted on the public available 3D plamprint database validate that our method can obtain the highest recognition performance among the state-of-the-art methods estimated.     

Palmprint authentication using a symbolic representation of images

A new branch of biometrics, palmprint authentication, has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we propose a new texture based approach for palmprint feature extraction, template representation and matching. An extension of the SAX (Symbolic Aggregate approXimation), a time series technology, to 2D data is the key to make this new approach effective, simple, flexible and reliable. Experiments show that by adopting the simple feature of grayscale information only, this approach can achieve an equal error rate of 0.3%, and a rank one identification accuracy of 99.9% on a 7752 palmprint public database. This new approach has very low computational complexity so that it can be efficiently implemented on slow mobile embedded platforms. The proposed approach does not rely on any parameter training process and therefore is fully reproducible. What is more, besides the palmprint authentication, the proposed 2D extension of SAX may also be applied to other problems of pattern recognition and data mining for 2D images.     

Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security

Biometric cryptosystems and cancelable biometrics are both practical and promising schemes to enhance the security and privacy of biometric systems. Though a number of bio-crypto algorithms have been proposed, they have limited practical applicability because they lack of cancelability. Since biometrics are immutable, the users whose biometrics are stolen cannot use bio-crypto systems anymore. Cancelable biometric schemes are of cancelability; however, they are difficult to compromise the conflicts between the security and performance. By embedded a novel cancelable palmprint template, namely “two dimensional (2D) Palmprint Phasor”, the proposed palmprint cryptosystem overcomes the lack of cancelability in existing biometric cryptosystems. Besides, the authentication performance is enhanced when users have different tokens/keys. Furthermore, we develop a novel dual-key-binding cancelable palmprint cryptosystem to enhance the security and privacy of palmprint biometric. 2D Palmprint Phasor template is scrambled by the scrambling transformation based on the chaotic sequence that is generated by both the user's token/key and strong key extracted from palmprint. Dual-key-binding scrambling not only has more robustness to resist against chosen plain text attack, but also enhances the secure requirement of non-invertibility. 2D Palmprint Phasor algorithm and dual-key-binding scrambling both increase the difficulty of adversary's statistical analysis. The experimental results and security analysis confirm the efficiency of the proposed scheme.     

基于Gabor小波变换和最佳鉴别特征的掌纹识别

提出了一种提取掌纹图像特征的方法,该方法的实现过程如下：首先,计算掌纹图像上均布离散位置的二维Gabor小波变换系数的幅值,将其作为掌纹图像的原始特征;其次,利用主分量分析实现Gabor小波特征的降维;最后,通过线性判别分析提取最有利于分类的最佳鉴别特征。实验结果表明了该方法的有效性。     

Palmprint feature extraction using 2-D Gabor filters

Biometric identification is an emerging technology that can solve security problems in our networked society. A few years ago, a new branch of biometric technology, palmprint authentication, was proposed (Pattern Recognition 32(4) (1999) 691) whereby lines and points are extracted from palms for personal identification. In this paper, we consider the palmprint as a piece of texture and apply texture-based feature extraction techniques to palmprint authentication. A 2-D Gabor filter is used to obtain texture information and two palmprint images are compared in terms of their hamming distance. The experimental results illustrate the effectiveness of our method.     

Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.