一种用于掌纹识别的线特征表示和匹配方法

作为一种较新的生物特征,掌纹可用来进行人的身份识别.在用于身份识别的诸多特征中,掌纹线,包括主线和皱褶,是最重要的特征之一.本文为掌纹识别提出一种有效的掌纹线特征的表示和匹配方法,该方法定义了一个矢量来表示一个掌纹上的线特征,该矢量称为线特征矢量(1ine feature vector,简称LFV).线特征矢量是用掌纹线上各点的梯度大小和方向来构造的.该矢量不但含有掌纹线的结构信息,而且还含有这些线的强度信息,因而,线特征矢量不但能区分具有不同线结构的掌纹,同时也能区分那些具有相似的线结构但各线强度分布不同的掌纹.在掌纹匹配阶段,用互相关系数来衡量不同线特征矢量的相似性.实验表明,LFV方法无论是在速度、精度,还是在存储量方面都能满足联机生物识别的要求.     

Wavelet Energy Feature Extraction and Matching for Palmprint Recognition

According to the fact that the basic features of a palmprint, including principal lines, wrinkles and ridges, have different resolutions, in this paper we analyze palmprints using a multi-resolution method and define a novel palmprint feature, which called wavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in different directions at different resolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each level to compute the weighted city block distance for recognition. The experimental results show that the order of the discriminabilities of each level WEF, from strong to weak, is the 4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation and translation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprint verification and palmprint identification, respectively. These results demonstrate the power of the proposed approach.     

Robust and efficient ridge-based palmprint matching

During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.     

Generating cancelable palmprint templates via coupled nonlinear dynamic filters and multiple orientation palmcodes

A novel scheme for generating cancelable palmprint templates is proposed in this paper. Firstly, a chaotic high speed stream cipher is implemented based on coupled nonlinear dynamic filters (CNDF), in which the CNDF are constructed to have flows inverse to each other. Secondly, renewable and privacy preserving palmprint templates are generated using the CNDF chaotic stream cipher with multiple orientation palmprint features obtained from a bank of Gabor filters and encoded in a phase-coding scheme. Compared with the standard palmprint templates, the cancelable templates have greater ability to discriminate palmprints from different hands by increasing the inter-class divergence of different palms more effectively, while maintaining the intra-class distance among palmprints of the same hands. Lastly, the matching stage is performed directly on the cancelable/encryption domain in parallel to accelerate matching and to protect user’s privacy. Several fusion rules are investigated for the matching scores of different directional PalmCodes to obtain the final matching score. Compared with Max, Min, Median and Product fusion rules, the Sum rule can greatly accelerate the speed and improve the performance Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels with a very strong ability to reissue palmprint templates. The proposed method can also be implemented at high speed, which satisfies the needs of real-time applications.     

Two novel characteristics in palmprint verification: datum point invariance and line feature matching

As the first attempt of automatic personal identification by palmprint, in this paper, two novel characteristics, datum point invariance and line feature matching, are presented in palmprint verification. The datum points of palmprint, which have the remarkable advantage of invariable location, are defined and their determination using the directional projection algorithm is developed. Then, line feature extraction and line matching are proposed to detect whether a couple of palmprints are from the same palm. Various palmprint images have been tested to illustrate the effectiveness of the palmprint verification with both characteristics.     

基于多线性主元分析和FLD的掌纹识别*

为了减少高维对计算成本的影响,同时提取有利于分类的判别特征,提出运用多线性主元分析(MPCA)与FLD相结合的方法进行掌纹识别。运用MPCA直接对掌纹张量进行降维和特征提取,低维特征向量作为FLD的输入,提取判别特征向量,计算特征向量间的余弦距离进行掌纹匹配。PolyU掌纹图像库的实验结果表明,与主元分析（PCA）、PCA+FLD、二维主元分析(2DPCA)、独立元分析（ICA）和MPCA相比,该算法的识别率(RR)最高为9991%,特征提取和匹配总时间为0398 s,满足实时系统的要求。     

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.     

Palmprint classification using principal lines

This paper proposes a novel algorithm for the automatic classification of low-resolution palmprints. First the principal lines of the palm are defined using their position and thickness. Then a set of directional line detectors is devised. After that we use these directional line detectors to extract the principal lines in terms of their characteristics and their definitions in two steps: the potential beginnings (“line initials”) of the principal lines are extracted and then, based on these line initials, a recursive process is applied to extract the principal lines in their entirety. Finally palmprints are classified into six categories according to the number of the principal lines and the number of their intersections. The proportions of these six categories (1-6) in our database containing 13,800 samples are 0.36%, 1.23%, 2.83%, 11.81%, 78.12% and 5.65%, respectively. The proposed algorithm has been shown to classify palmprints with an accuracy of 96.03%.     

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…     

Palmprint matching using feature points and SVD factorisation

In this paper, a new palmprint matching system based on the extraction of feature points is suggested. Using a scale-space representation, the points in question are corners formed by the intersection of creases and lines. Unlike minutiae, such points can still be extracted even on low resolution palmprints. Matching is carried out using an SVD factorisation of a proximity matrix and takes account of the coordinates of the detected points and their local texture. Our experiments have yielded some very good results evidenced by an EER of 0.10%.     

Concavity-orientation coding for palmprint recognition

Efficient feature extraction strategies play an important role in palmprint recognition systems. Among various feature extraction methods, orientation methods such as Competitive Code and Half Orientation Code are the baseline ones. They encode responses of a bank of orientational filters into a binary representation and can match a test palmprint sample in real-time with a relatively good accuracy. However, they use the orientation information based upon this idea that palmprints encompass only straight lines with different orientations, whereas in reality, the majority of palm’s lines are curved. This observation naturally brings the idea that the concavity and orientation features as different aspects of palmprints curves might provide more reliable and discriminative representations in palmprint recognition. Motivated by this idea, in this work we investigate the use of the concavity feature in different orientations for palmprint recognition. The experimental results, which are applied on PolyU II, 2D/3D PolyU, and blue and near infrared range images from Multispectral PolyU palmprint databases prove the efficiency of this idea compared to other coding-based methods.     

Characterization of palmprints by wavelet signatures via directional context modeling

The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.     

A study of identical twins’ palmprints for personal verification

Automatic biometric systems based on human characteristics for personal identification have attracted great attention. Their performance highly depends on the distinctive information in the biometrics. Identical twins having the closest genetics-based relationship are expected to have maximum similarity in their biometrics. Classifying identical twins is a challenging problem for some automatic biometric systems. Palmprint has been studied for personal identification for over seven years. Most of the previous research concentrates on algorithm development. In this paper, we systemically examine palmprints from the same DNA for automatic personal identification and to uncover the genetically related palmprint features. The experimental results show that the three principal lines and some portions of weak lines are genetically related features but our palms still contain rich genetically unrelated features for classifying identical twins.     

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.     

Palmprint verification using hierarchical decomposition

A reliable and robust personal verification approach using palmprint features is presented in this paper. The characteristics of the proposed approach are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, a flatbed scanner is adopted as an input device for capturing palmprint images; it has the advantages of working without palm inking or a docking device. In the proposed approach, two finger-webs are automatically selected as the datum points to define the region of interest (ROI) in the palmprint images. The hierarchical decomposition mechanism is applied to extract principal palmprint features inside the ROI, which includes directional and multi-resolution decompositions. The former extracts principal palmprint features from each ROI. The latter process the images with principal palmprint feature and extract the dominant points from the images at different resolutions. A total of 4800 palmprint images were collected from 160 persons to verify the validity of the proposed palmprint verification approach and the results are satisfactory with acceptable accuracy (FRR: 0.75% and FAR: 0.69%). Experimental results demonstrate that our proposed approach is feasible and effective in palmprint verification.     

Document scanners for minutiae-based palmprint recognition: a feasibility study

Highly expensive capturing devices and barely existent high-resolution palmprint datasets have slowed the development of forensic palmprint biometric systems in comparison with civilian systems. These issues are addressed in this work. The feasibility of using document scanners as a cheaper option to acquire palmprints for minutiae-based matching systems is explored. A new high-resolution palmprint dataset was established using an industry-standard Green Bit MC517 scanner and an HP Scanjet G4010 document scanner. Furthermore, a new enhancement algorithm to attenuate the negative effect of creases in the process of minutiae extraction is proposed. Experimental results highlight the potentialities of document scanners for forensic applications. Advantages and disadvantages of both technologies are discussed in this context as well.

     

基于主线特征的双向匹配的掌纹识别新方法

掌纹识别是利用人的手掌掌纹图像对其身份进行认证的一种生物特征识别技术，目前的掌纹研究主要集中在掌纹特征线的提取算法上，而对特征线的筛选和匹配的问题讨论较少，掌纹上的纹线比较复杂，深浅粗细长短不一，实施任何一种边缘提取算法都要考虑纹线的取舍问题，首先介绍了提出的应用最大内切圆对掌纹有效区域进行分割和对准的方法，较好地解决了掌纹的定位问题，然后提出了掌纹特征线族的概念，用以刻画掌纹上的主要特征，从而将掌纹纹线特征分为主要特征和次要特征．通过对主要特征与全部特征的双向匹配，给出最终的识别结果，将该方法与之前提出的基于傅里叶变换的方法在自行研制的掌纹采样设备所采集的掌纹库(90人450幅)上进行了比较实验，实验结果证明新方法可以处理原方法无法定位的掌纹图像，同时识别率也有明显提高。