基于LEM的在线掌纹识别

主要研究利用掌纹对人进行身份鉴定。在掌纹的各种特征中，线特征是一种非常重要的特征，但由于掌纹线不规则，几乎不能用数学进行精确的刻画，因此提出了一种用直线段去近似掌纹线的方法，利用线段Hausdorff距离方法去匹配这些线段集，并对其进行了改进。实验的结果表明了该方法的有效性。     

基于ICA的在线掌纹识别

个人身份认证和鉴别在现在社会显示着重要的作用,作为一种准确而可靠的个人鉴定方式,生物识别已经引起了广泛的注意.掌纹作为一种相对较新的生物识别技术也有着独特的优点.而掌纹特征的提取和选择是整个识别中最关键的一个环节,主要利用ICA（独立主成分分析）方法对掌纹进行特征提取,实验证明,相比较PCA（主成分分析）方法,基于ICA方法具有更高的识别效率.     

基于Gabor滤波器和LBP的分级掌纹识别

本文首次提出将局部二进制模式(LBP)应用到掌纹识别中。借鉴分级检索的思想,先采用Gabor滤波器提取掌纹的全局能量特征,后采用LBP算子提取局部特征实现两次分类。实验结果表明,与单纯的Gabor滤波器方法相比,系统的识别率可进一步提高。     

An improved palmprint recognition system using iris features

This paper presents a bimodal biometric recognition system based on the extracted features of the human palmprint and iris using a new graph-based approach termed Fisher locality preserving projections (FLPP). This new technique employs two graphs with the first being used to characterize the within-class compactness and the second dedicated to the augmentation of the between-class separability. By applying the FLPP, only the most discriminant and stable palmprint and iris features are retained. FLPP was implemented on the frequency domain by transforming the extracted region of interest extraction of both biometric modalities using Fourier transform. Subsequently, the palmprint and iris features vectors obtained are matched with their counterpart in the templates databases and the obtained scores are fused to produce a final decision. The proposed combination of palmprint and iris patterns has shown an excellent performance compared to unimodal palmprint biometric recognition. The system was evaluated on a database of 108 subjects and the experimental results show that our system performs very well and achieves a high accuracy expressed by an equal error rate of 0.00%.     

LBP与鉴别模式结合的热红外人脸识别

为了提取具有鉴别能力的红外人脸图像局部结构特征,提出一种基于LBP(local binary pattern)鉴别模式的红外人脸识别方法。传统的LBP均匀模式,提取自然图像中占主导地位的信息用于识别,但占主导地位的信息不一定是最适合识别的。为了提取有效的鉴别模式特征,基于监督学习的思想,在LBP模式下引入可分性标准,对不同LBP模式进行有效的模式选择,从而抽取适合识别的鉴别模式。最后,为了利用人脸的空间位置信息,结合分块和直方图技术得到最后的识别特征。实验结果表明,本文鉴别模式可以提取更适合识别的特征,识别性能优于传统的基于均匀模式的LBP方法。     

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.     

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.     

Collaborative representation with HM-LBP features for palmprint recognition

A novel collaborative representation model with hierarchical multiscale local binary pattern (HM-LBP) for palmprint recognition is proposed in this paper. HM-LBP can retrieve useful information from non-uniform patterns and reduce the influence of gray scale, rotation and illumination. The HM-LBP feature of palmprint is extracted, and its dimension is reduced by principal component analysis. And then, a collaborative classification with HM-LBP is presented to fully exploit the discrimination information. The proposed algorithm is evaluated on the Hong Kong Polytechnic University database (v2) to test its feasibility and performance. The results show that the algorithm can achieve ideal recognition accuracy of 100% and the speediness is able to fit for the real-time palmprint recognition system.     

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.     

Robust partial fingerprint recognition using wavelet SIFT descriptors

Even though there have been many advancements in the fingerprint identification, matching of the partial and overlapped fingerprints is yet to be resolved. This problem has drawn more attention with the evolution of small-sized scanners. The research attempts to resolve this issue whenever there are no core points available in the partial fingerprints. Moreover, the scaled or rotated versions of the image may also lead to poor matching performance. In order to overcome these challenges, a robust partial fingerprint matching method using the scale-invariant feature transform (SIFT) features of the wavelet decomposed image is proposed. The performance of the proposed method is compared with the baseline (minutiae) and the state-of-the-art (SIFT) techniques for partial fingerprint recognition. The method is experimented for different cropped levels (horizontal, vertical, diagonal, quadrant-wise random cuts and the region around a core point) of the image. Experimental studies with 100 subjects show that the proposed method improves recognition accuracy and reduces false acceptance rate (FAR) and false rejection rate (FRR) even for images with 75% occlusion.     

掌纹样本采集技术及预处理技术的分析与研究

对掌纹识别技术中的采样方法、色彩校正、亮度分布不均匀校正、手形朝向校正及目标区域获取这些技术进行了分析和研究,提出了改进的采样方法、基于采样统计的掌纹色斑校正方法、基于亮度分布估计的图像内亮度分布不均匀的校正方法、基于数学形态学的手形方向校正及目标区域获取方法。实验结果表明这些方法取得了良好的效果。     

Age-invariant face recognition using multiple descriptors along with modified dimensionality reduction approach

Multimedia Tools and Applications - Face recognition on the basis of age variation is a significant yet challenging issue. One among the effective methods to age-invariant face recognition is to...     

基于二维局部保留映射的小样本掌纹识别

小样本生物识别是现实应用中一个较难解决的问题,通过有限训练样本很难得到满意的识别结果。因此,提出了一种新的小样本掌纹识别方法,利用改进的二维局部保留映射（I2DLPP）提取特征,并用支持向量机（SVM）分类。改进的二维局部保留映射是通过同时在行和列方向上进行2DPCA和2DLPP的投影实现的,从而降低了计算复杂度与特征维数;并且构建最近邻图是以图像内部的列为节点,保留更多内部流形结构,改善了识别效果。SVM是针对小样本识别的非常有效的分类工具,将两者结合可以显著提高小样本掌纹识别精度。实验结果证明了该方法的有效性。     

Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm

Pattern Analysis and Applications - Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint...     

Human gait recognition using GEI-based local multi-scale feature descriptors

Multimedia Tools and Applications - Human gait recognition is a biometric technique for persons identification based on their walking manner. This paper proposes a novel gait recognition approach...     

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.     

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.