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.     

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.     

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。     

Stereo-based palmprint recognition in various 3D postures

In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

基于近邻三值模式和协作表示的三维掌纹识别

针对二维掌纹图像存在易伪造、抗噪能力差的问题，提出一种基于近邻三值模式（NTP）和协作表示的三维掌纹识别方法。首先，利用形状指数把三维掌纹的表面几何信息映射成二维数据，以弥补常用均值或高斯曲率映射无法精确描述三维掌纹特征的缺陷；其次，对形状指数图作分块处理，利用近邻三值模式提取分块形状指数图的纹理特征；最后，利用协作表示的方法进行特征分类。在三维掌纹库上和经典算法进行的对比实验中，该方法的识别率为99.52%，识别时长为0.6738 s，优于其他算法；在识别率方面，与经典的局部二值模式（LBP）、局部三值模式（LLTP）、CompCode、均值曲率图（MCI）法相比分别提高了7.77%、6.02%、5.12%和3.97%；在识别时间方面，与Homotopy、对偶增广拉格朗日法（DALM）、SpaRSA方法相比分别降低了6.7 s、15.9 s和61 s。实验结果表明，所提算法具有良好的特征提取和分类能力，能够有效地提高识别精度并减少识别时间。     

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。     

Hand-based single sample biometrics recognition

Currently, single sample biometrics recognition (SSBR) has emerged as one of the major research contents. It may lead to bad recognition result. To solve this problem, we present a novel approach by fusing two kinds of hand-based biometrics, i.e., palmprint and middle finger. We obtain their discriminant features by combining statistical information and structural information of each modal which are extracted using locality preserving projection (LPP) based on wavelet transform (WT). In order to reduce the influence of affine transform, we utilize mean filtering to enhance the robustness of structural information to improve the discriminant ability of palmprint high-frequency sub-bands. The two types of features are then fused at score level for the final hand-based SSBR. The experiments on the hand image database that contains 1,000 samples from 100 individuals show that the proposed feature extraction and fusion methods lead to promising performance.     

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.     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

基于双目被动立体视觉的三维人脸重构与识别

提出一种基于双目被动视觉的三维人脸识别方法, 该方法采用非接触式的人脸信息采集技术, 利用图像中弱特征检测方法实现双目视觉中的人脸检测与初步视差估计, 运用基于复小波的相位相关技术对人脸表面进行亚像素级小区域匹配, 重建人脸三维点云信息. 通过可调训练次数的神经网络技术实现多层次人脸曲面重建, 并结合人脸2D图像对重构曲面进行仿射归一, 继而迭代地进行特征提取与识别过程. 实验结果表明, 双目视觉方法使人脸信息采集过程友好隐蔽; 在对应点匹配中, 运用复小波的相位相关算法可获得密集的亚像素精度配准点对, 用神经网络方法可正确重建人脸曲面. 识别过程对环境以及人脸位姿表情等鲁棒性强. 该系统成本十分低廉, 适合在许多领域推广应用.     

Improved discriminant locality preserving projections for face and palmprint recognition

We propose in this paper two improved manifold learning methods called diagonal discriminant locality preserving projections (Dia-DLPP) and weighted two-dimensional discriminant locality preserving projections (W2D-DLPP) for face and palmprint recognition. Motivated by the fact that diagonal images outperform the original images for conventional two-dimensional (2D) subspace learning methods such as 2D principal component analysis (2DPCA) and 2D linear discriminant analysis (2DLDA), we first propose applying diagonal images to a recently proposed 2D discriminant locality preserving projections (2D-DLPP) algorithm, and formulate the Dia-DLPP method for feature extraction of face and palmprint images. Moreover, we show that transforming an image to a diagonal image is equivalent to assigning an appropriate weight to each pixel of the original image to emphasize its different importance for recognition, which provides the rationale and superiority of using diagonal images for 2D subspace learning. Inspired by this finding, we further propose a new discriminant weighted method to explicitly calculate the discriminative score of each pixel within a face and palmprint sample to duly emphasize its different importance, and incorporate it into 2D-DLPP to formulate the W2D-DLPP method to improve the recognition performance of 2D-DLPP and Dia-DLPP. Experimental results on the widely used FERET face and PolyU palmprint databases demonstrate the efficacy of the proposed methods.     

Double-cohesion learning based multiview and discriminant palmprint recognition

Palmprint recognition has been widely used in security authentication. However, most of the existing palmprint representation methods are focused on a special application scenario using the hand-crafted features from a single-view. If the features become weak as the application scenario changes, the recognition performance will be degraded. To address this problem, we propose to comprehensively exploit palmprint features from multiple views to improve the recognition performance in generic scenarios. In this paper, a novel double-cohesion learning based multiview and discriminant palmprint recognition (DC_MDPR) method is proposed, which imposes a double-cohesion strategy to reduce the inter-view margins for each subject and the intra-class margins for each view. In this way, for each subject, the features from different views can be closer to each other in the binary-label space. Meanwhile, for each view, the features sharing the same label information can move towards each other by imposing a neighbor graph regularization. The proposed method can be flexibly applied to any type of palmprint feature fusion. Moreover, it presents the multiview features in a low-dimensionality sub-space, effectively reducing the computational complexity. Experimental results on various palmprint databases have shown that the proposed method can always achieve the best recognition performance compared to other state-of-the-art algorithms.     

Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition

Recently, multi-modal biometric fusion techniques have attracted increasing atove the recognition performance in some difficult biometric problems. The small sample biometric recognition problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion recognition approach is a rather effective solution for the small sample recognition problem.     

Face recognition based on Kinect

In this paper, we present a new algorithm that utilizes low-quality red, green, blue and depth (RGB-D) data from the Kinect sensor for face recognition under challenging conditions. This algorithm extracts multiple features and fuses them at the feature level. A Finer Feature Fusion technique is developed that removes redundant information and retains only the meaningful features for possible maximum class separability. We also introduce a new 3D face database acquired with the Kinect sensor which has released to the research community. This database contains over 5,000 facial images (RGB-D) of 52 individuals under varying pose, expression, illumination and occlusions. Under the first three variations and using only the noisy depth data, the proposed algorithm can achieve 72.5 % recognition rate which is significantly higher than the 41.9 % achieved by the baseline LDA method. Combined with the texture information, 91.3 % recognition rate has achieved under illumination, pose and expression variations. These results suggest the feasibility of low-cost 3D sensors for real-time face recognition.     

基于二维双向FLD的掌纹识别方法

采用二维双向Fisher线性判别分析对掌纹图像进行特征提取,即通过在水平和垂直2 个方向上各执行1 次二维Fisher线性判别分析,能消除掌纹图像行和列的相关性。运用Fisher准则选取更适合于分类的矩阵分量,将特征信息压缩到图像矩阵的左上角,缩小了特征的维数。测试结果表明,该方法具有更高的识别率和更低的计算复杂度。     

加权自适应CS-LBP与局部判别映射相结合的掌纹识别方法

提取掌纹的最佳低维分类特征一直是掌纹识别研究领域的一个重要方向。针对掌纹图像具有丰富的纹理特征特点,提出一种基于加权自适应中心对称局部二值模式(WACS-LBP)与局部判别映射(LDP)相结合的掌纹识别方法。首先将掌纹感兴趣(ROI)图像分成大小均匀的小区域,利用自适应中心对称局部二值模式(ACS-LBP)算法获取不同区域的纹理特征直方图和权值,经过加权连接得到ROI的加权纹理特征直方图向量;再利用LDP算法对得到的特征向量进行维数约简;最后利用K-最近邻分类器进行掌纹识别。在掌纹公开数据库上进行实验,正确识别率高达97%以上。实验结果表明,该方法不仅是有效、可行的,而且研究思路比较明确。     

PCA-based image recombination for multimodal 2D + 3D face recognition

Most of the existing approaches of multimodal 2D + 3D face recognition exploit the 2D and 3D information at the feature or score level. They do not fully benefit from the dependency between modalities. Exploiting this dependency at the early stage is more effective than the later stage. Early fusion data contains richer information about the input biometric than the compressed features or matching scores. We propose an image recombination for face recognition that explores the dependency between modalities at the image level. Facial cues from the 2D and 3D images are recombined into a more independent and discriminating data by finding transformation axes that account for the maximal amount of variances in the images. We also introduce a complete framework of multimodal 2D + 3D face recognition that utilizes the 2D and 3D facial information at the enrollment, image and score levels. Experimental results based on NTU-CSP and Bosphorus 3D face databases show that our face recognition system using image recombination outperforms other face recognition systems based on the pixel- or score-level fusion.     

Local image quality measurement for multi-scale forensic palmprints

Numerous studies show that palmprint image quality has a significant effect on every stage of a palmprint recognition system. Although some palmprint image quality measurement(PIQM) methods are proposed, some insufficiency in classification accuracy occurs and attention to detail in measuring local area image quality of multi-scale palmprint images is lacking. On the one hand, the classification accuracy is not very high for 2-class classification and it degrades significantly as the number of classes increases. On the other hand, local area image quality measurement of multi-scale palmprint images has not yet been resolved since the handcrafted features designed through domain knowledge usually works for certain scale image blocks. Meanwhile, the intricate domain knowledge used in the previous methods is difficult for some common users to acquire. In this paper, we propose an end-to-end deep-learning method of strengthening representation ability that learns more abstract, essential, and reliable features to measure the local image quality for multi-scale forensic palmprints. Popular convolutional neural networks (CNNs) are considered because of their powerful representation ability in learning complex features. However, the powerful existing CNNs usually have complex architectures with a large amount of parameters, which need the support of high-performance computers. They are not suitable to be used directly for palmprint image quality assignment and the follow-up palmprint recognition work, which prefers real-time response on commonly available personal computers or even mobile devices. Hence, a new lightweight CNN must be designed to achieve a trade-off between high classification accuracy and practical usability. Considering the attributes of under-processed input images, we reduce the weight of the CNN architecture by reducing the amount of some parameters, and finally a lightweight CNN is designed. As a result, a raw rectangular palmprint image of variable size can be put into the trained model directly and a quality label quickly predicted with high accuracy. After comparison with previous methods, results show that the proposed method can deal with un-pre-processed raw images of a multi-scale input size. Furthermore, it can acquire a richer amount of quality classes with a higher accuracy, which are stable on many different datasets. It also leads to finer and more precise full palmprint image quality maps when compared to previous methods.

     

A coding-guided holistic-based palmprint recognition approach

In the past few years, the need for accuracy and robustness against luminosity variations has drawn a considerable share of the palmprint research toward coding-based approaches. However, on the downside coding-based approaches require a high computational cost. On the contrary, while holistic-based palmprint recognition methods are easy to implement and have low computational burden, they usually do not result in a highly desirable accuracy. As a result, more recently hybridization of the holistic-based and coding-based methods has gained a boost. These hybridization schemes take advantages of both holistic and coding information to achieve a better performance. However, their computational burden due to incorporating the coding approach is still much heavier than the holistic methods. In this paper, we propose a new hybridization scheme based on Anisotropic Filter (AF) coding and the two-phase test sample representation (TPTSR) for the palmprint identification. In our scheme, the coding-based method is only applied on a super narrowed gallery in order to measure the classification confidence for a given test sample. Then, we apply our Guided Holistic (GH)-based method for classifying the test sample if the holistic-based algorithm is not sufficiently confident. Experimental results demonstrate the efficiency of our method in enhancing both the complexity cost and the accuracy of the results.