Orientation selection using modified FCM for competitive code-based palmprint recognition

Coding-based methods are among the most promising palmprint recognition methods because of their small feature size, fast matching speed and high verification accuracy. The competitive coding scheme, one representative coding-based method, first convolves the palmprint image with a bank of Gabor filters with different orientations and then encodes the dominant orientation into its bitwise representation. Despite the effectiveness of competitive coding, few investigations have been given to study the influence of the number of Gabor filters and the orientation of each Gabor filter. In this paper, based on the statistical orientation distribution and the orientation separation characteristics, we propose a modified fuzzy C-means cluster algorithm to determine the orientation of each Gabor filter. Since the statistical orientation distribution is based on a set of real palmprint images, the proposed method is more suitable for palmprint recognition. Experimental results indicate that the proposed method achieves higher verification accuracy while compared with that of the original competitive coding scheme and several state-of-the-art methods, such as ordinal measure and RLOC. Considering both the computational complexity and the verification accuracy, competitive code with six orientations would be the optimal choice for palmprint recognition.     

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.     

On accurate orientation extraction and appropriate distance measure for low-resolution palmprint recognition

Orientation feature has been demonstrated to be one of the most effective features for low resolution palmprint recognition. In this paper, using steerable filter, we investigate the accurate orientation extraction and appropriate distance measure problems for effective palmprint recognition. First, we use high order steerable filter to extract accurate continuous orientation, and quantify it into discrete representation. Then, for effective matching of accurate orientations, we propose a generalized orientation distance measure. We further extend the distance measure for matching of discrete orientations, and show that several existing distance measures can be viewed as its special cases. Experimental results on both Hong Kong PolyU and CASIA palmprint databases show that the proposed method can obtain state-of-the-art verification accuracy. With the support of a look up table, the proposed method also enables small template size and satisfactory matching speed for practical applications.     

Robust palmprint identification based on directional representations and compressed sensing

In this paper, we propose a novel approach for palmprint recognition, which contains two interesting components: directional representation and compressed sensing. Gabor wavelets can be well represented for biometric image for their similar characteristics to human visual system. However, these Gabor-based algorithms are not robust for image recognition under non-uniform illumination and suffer from the heavy computational burden. To improve the recognition performance under the low quality conditions with a fast operation speed, we propose novel palmprint recognition approach using directional representations. Firstly, the directional representation for palmprint appearance is obtained by the anisotropy filter, which is robust to drastic illumination changes and preserves important discriminative information. Then, the principal component analysis (PCA) is used for feature extraction to reduce the dimensions of the palmprint images. At last, based on a sparse representation on PCA feature, the compressed sensing is used to distinguish palms from different hands. Experimental results on the PolyU palmprint database show the proposed algorithm have better performance than that of the Gabor based methods.     

Fusion of phase and orientation information for palmprint authentication

This paper presents a novel approach for palmprint authentication based on the fusion of the phase and orientation information. In the proposed approach, the phase information (called FusionCode or PhaseCode) of a palmprint is extracted by using four 2D Gabor filters with different orientations, and at the same time the orientation information (called OrientationCode) of the palmprint is also extracted. The PhaseCode and the OrientationCode are fused to make a new feature, called the Palmprint Phase and Orientation Code (PPOC). At the matching stage, a modified Hamming distance is defined to measure the similarity of two PPOCs. This approach is tested on a palmprint database containing 7,605 samples and the experimental results show that the accuracy and speed of the PPOC approach can satisfy the requirements of a real time biometric system.     

采样式多通道Gabor滤波对掌纹的特征提取和分类

掌纹识别已被证实为最方便和有效的身份识别方法之一。根据掌纹的性质提出了一种掌纹方向特征提取的新方法,该方法首先利用选取掌纹中最拟合椭圆的方法寻找感兴趣区域,然后利用适应人感官系统的多通道采样式Gabor滤波器进行滤波,并提出用根据掌纹纹理和方向特性动态选取Gabor滤波器参数的方法来设计滤波器。在滤波过程中,从不同分辨率入手,利用不同方向和宽度的滤波器分别对掌纹的主线、褶皱、嵴线进行提取,在极坐标系下用改进的环行方向投影算法计算块能量,并且进行编码。经过模糊C均值聚类方法验证,结果表明,该方法对于掌纹具有很强的识别能力。     

基于GIDBC的掌纹识别算法研究

针对采用分形维数作为特征描述掌纹信息不准确的问题,对差分盒子维进行改进提高特征区分性.此外,由于采用单一的特征不足以描述掌纹纹理,引入Gabor变换,提出一种基于Gabor变换与改进差分盒子维(GIDBC,Gabor improved differential box counting)相结合的掌纹识别算法.通过在PolyU掌纹图像库上实验,与传统高性能算法比较,本算法识别率最高可达到99.78％,表明了本文方法的有效性,同时特征提取与匹配时间为338 ms,满足实时性要求.     

A combination of Genetic Algorithm,Particle Swarm Optimization and Neural Network for palmprint recognition

In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.

     

一种新的掌纹特征提取方法研究

提出一种基于Gabor小波和改进的广义K-L变换的掌纹识别方法。该方法首先对测试样本的掌纹ROI灰度图像进行Gabor小波变换,得到其Gabor特征向量,然后利用改进的广义K-L变换方法将高维特征向量变换到低维空间,最后将得到的低维特征向量利用欧氏距离法与训练样本库中的特征向量作匹配识别。该方法首次将基于时频变换的特征提取算法与基于子空间的特征提取算法结合起来,既充分利用了Gabor函数优良的特征提取性能,又有效解决了高维特征的降维处理问题。通过使用自行采集的数据库对该方法作对比实验,获得了94%的识别率     

A mixture model for population codes of Gabor filters

Population coding is a coding scheme which is ubiquitous in neural systems, and is also of more general use in coding stimuli, for example in vision problems. A population of responses to a stimulus can be used to represent not only the value of some variable in the environment, but a full probability distribution for that variable. The information is held in a distributed and encoded form, which may in some situations be more robust to noise and failures than conventional representations. Gabor filters are a popular choice for detecting edges in the visual field for several reasons. They are easily tuned for a variety of edge widths and orientations, and are considered a close model of the edge filters in the human visual system. In this paper, we consider population codes of Gabor filters with different orientations. A probabilistic model of Gabor filter responses is presented. Based on the analytically derived orientation tuning function and a parametric mixture model of the filter responses in the presence of local edge structure with single or multiple orientations a probability density function (pdf) of the local orientation in any point (x, y) can be extracted through a parameter estimation procedure. The resulting pdf of the local contour orientation captures not only angular information at edges, corners or T-junctions but also describes the certainty of the measurement which can be characterized in terms of the entropy of the individual mixture components.     

基于Gabor滤波器与LDP掌纹可撤销模板生成方法

针对掌纹身份认证中存在着识别率和安全性较差的问题,提出一种基于多方向的Gabor滤波和局部方向模式（Local Directional Pattern,LDP）的自适应阈值特征编码方法mLGDP,在此基础上,进一步提出一种基于多方向Gabor滤波和LDP方法的自适应阈值差值特征编码方法mDLGDP,并将这两种方法的特征相融合,有效增强了原有掌纹模板间的多样性和识别率。通过对图像的特征编码进行分块处理,提取特征向量并二值化,再采用Bloom滤波器实现多对一映射和对掌纹图像的位置置乱,将得到置乱结果矩阵和用户密钥通过卷积运算进行不可逆变换,最终获得掌纹图像的可撤销模板。理论分析和实验表明,即使在密钥丢失时,分别使用两种改进方法依然可以保持较高的识别率,当使用两种特征相融合的方法时,识别率能够得到有效提高,且具有更好的安全性。     

基于融合的显微图像Gabor边缘提取算法

不同参数Gabor滤波器都具有各自的频率选择和方向选择特性,图像中纹理基元可以利用多个方向和中心频率Gabor滤波器组提取出来的频谱值来表示.据此提出一种适应于显微图像的Gabor滤波边缘检测算法.使用特定不同方向Gabor滤波边缘提取算子对图像进行边缘信息提取,获得不同方向上边缘特征信息,对其进行分析和融合提取图像边缘.运用该方法时所获不同方向的图像边缘进行自适应融合,获得的图像边缘较理想,模糊的边缘得到增强,并有效地消除了噪声.实验结果表明,该算法对显微图像处理有效,检测到的边缘清晰.     

Palmprint identification using feature-level fusion

In this paper, we propose a feature-level fusion approach for improving the efficiency of palmprint identification. Multiple elliptical Gabor filters with different orientations are employed to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code. The similarity of two Fusion Codes is measured by their normalized hamming distance. A dynamic threshold is used for the final decisions. A database containing 9599 palmprint images from 488 different palms is used to validate the performance of the proposed method. Comparing our previous non-fusion approach and the proposed method, improvement in verification and identification are ensured.     

Orientation Field Code Hashing: A Novel Method for Fast Palmprint Identification

For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification.By investigating hashing-based algorithms,we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable.Secondly,we propose a window-based feature measurement for rapid searching of the target.We explore the influence of parameters related to hashing-based palmprint identification.We have carried out a number of experiments on the Hong Kong Poly U large-scale database and the CASIA palmprint database plus a synthetic database.The results show that on the Hong Kong Poly U large-scale database,the proposed method is about 1.5 times faster than the state-of-the-art ones,while achieves the comparable identification accuracy.On the CASIA database plus the synthetic database,the proposed method also achieves a better performance on identification 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.     

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.     

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.     

基于区域特征映射的模糊掌纹识别方法

针对掌纹在非接触采集时易出现模糊现象从而导致系统识别性能降低的问题,建立了区域到点的特征映射模型,提出了一种基于区域特征映射(Region feature map, RFM)的模糊掌纹识别方法.首先根据图像的模糊原理,建立等价的模糊模型,获取模糊掌纹; 然后使用RFM对模糊掌纹进行操作,将高维的区域特征映射到低维的点特征; 最后,采用归一化相关性分类器对掌纹所属类别进行判定识别.使用模糊模型对PolyU掌纹库进行处理得到PolyU模糊掌纹库,并分别在PolyU掌纹库和PolyU模糊掌纹库上进行测试,识别结果较为稳定. 在模糊掌纹库上,本文算法的等错误率(Equal error rate, EER)最小可达0.9069%,优于传统算法,且进行一次识别的时间为33.95ms,得到的特征数据维数较小,降低了算法复杂度,表明了本文算法的有效性和实时性.     

基于Gabor局部相对特征的掌纹识别

Gabor变换是掌纹识别中提取纹理特征的一个重要工具,但其性能易受图像的变化以及不均衡噪声等因素影响,因此提出了一种基于Gabor局部相对特征的掌纹识别算法。该算法对原始图像进行微尺度不变Gabor滤波;结合分形学的思想,将滤波后的图像分成大小相等的子域,每个子域又分成多个相同的子块,计算每个子块与它所在子域的相对方差,将所有子块的相对方差排列组成表征图像的特征向量进行识别。该算法将微尺度不变与局部相对特性统一,所提取的特征对各种变化有很强的鲁棒性,提高了识别精度和效率。实验使用北京交通大学BJTU_PalmprintDB证明该算法的有效性。