Elsevier

Expert Systems with Applications

Volume 82, 1 October 2017, Pages 151-161
Expert Systems with Applications

Finger-vein verification based on the curvature in Radon space

https://doi.org/10.1016/j.eswa.2017.03.068Get rights and content

Highlights

  • We proposed a new approach to extract finger-vein pattern.

  • The proposed approach is based on the curvature in Radon space.

  • This paper rigorously evaluates the verification performance of our approach.

Abstract

Finger-vein verification has drawn increasing attention because it is highly secured and private biometric in practical applications. However, as the imaging environment is affected by many factors, the captured image contains not only the vein pattern but also the noise and irregular shadowing which can decrease the verification accuracy. To address this problem, in this paper, we proposed a new finger-vein extraction approach which detects the valley-like structures using the curvatures in Radon space. Firstly, given a pixel, we obtain eight patches centered on it by rotating a window along eight different orientations and project the resulting patches into Radon space using the Radon transform. Secondly, the vein patches create prominent valleys in Radon space. The vein patterns are enhanced according to the curvature values of the valleys. Finally, the vein network is extracted from the enhancing image by a binarization scheme and matched for personal verification. The experimental results on both contacted and contactless finger-vein databases illustrate that our approach can significantly improve the accuracy of the finger-vein verification system.

Introduction

With the tremendous growth in the demand for secured systems, the automatic personal verification using biometrics has drawn increasing attention and become one of the most critical and challenging tasks. The physical and behavioral characteristics of people such as face, fingerprint, signature and gait have been widely applied for identification of criminals as a tool by law. Some researchers have explored new biometrics features and traits. Currently, a number of biometric characteristics have been employed to achieve the verification task and can be broadly categorized in two categories; (1) extrinsic biometric features, i.e. face, fingerprint, palm-print and iris. (2) intrinsic biometric features, i.e. finger-vein, hand-vein and palm-vein. Extrinsic biometric feature are susceptible to spoof attacks because the fake fingerprints, palm-prints and face images can successfully cheat the verification system. Therefore, the usage of extrinsic biometric feature generate some concerns on privacy and security in practical application. On the other hand, it is difficult to acquire without the knowledge of an individual and forge intrinsic biometrics characteristics (finger-vein, hand-vein and palm-vein). In addition, the key point in practical applications is that the biometrics trait must provide the high collectability and convenience for user. Therefore, the finger-vein biometric has emerged as a promising alternative for personal verification. Firstly, it is difficult for user to leave crucial information when he/she interact with the finger-vein capturing device. Secondly, using finger-vein for verification is convenient to be captured in practical applications. Therefore, the finger-vein verification is a highly secured, private and convenient biometric for the practical applications.

The finger-vein is under surface and not easily observed in visible light, but it can be captured using the infrared light emitting diode (LED) and charge coupled device (CCD) camera (Kono, Ueki, & Umemura, 2002). Finger-vein verification is still a challenging task because the finger-vein acquisition process is inherently affected by a number of factors: environmental illumination (Hashimoto, 2006, Huang, Dai, Li, Tang, Li, 2010, Song, Kim, Kim, Choi, Kong, Lee, 2011, Yu, Qing, Zhang, 2008), ambient temperature (Kumar, Zhou, 2012, Miura, Nagasaka, Miyatake, 2007, Mulyono, Jinn, 2008, Song, Kim, Kim, Choi, Kong, Lee, 2011), light scattering in imaging finger tissues (Cheong, Prahl, Welch, 1990, Lee, Park, 2011, Yang, Shi, 2014), physiological changes (Kumar, Zhou, 2012, Miura, Nagasaka, Miyatake, 2007) and user behavior (Hashimoto, 2006, Huang, Dai, Li, Tang, Li, 2010, Mulyono, Jinn, 2008). In practical application, these factors are not controlled and/or avoided, so some noise and irregular shadowing are produced in many acquired finger-vein images. For example, the sensor and circuitry of a scanner or digital camera can produce the electronic noise, and the irregular shading is mainly caused by the varied thickness of the finger and/or the uneven illumination. In general, these noise and irregular shadowing will ultimately compromise the performance of the automatic authentication system. Currently, there are several schemes such quality assessments (Nguyen, Park, Shin, Park, 2013, Qin, Li, Kot, Qin, 2012, Yang, Yang, Yin, Xiao, 2013), image restoration (Lee, Park, 2009, Lee, Park, 2011, Nguyen, Park, Shin, Park, 2013, Yang, Shi, 2014) and image feature extraction (Kono, Ueki, Umemura, 2002, Kumar, Zhou, 2012, Miura, Nagasaka, Miyatake, 2007, Miura, Nagasaka, Miyatake, 2004, Mulyono, Jinn, 2008, Qin, Qin, Yu, 2011, Yu, Qin, Cui, Hu, 2009, Yu, Qing, Zhang, 2008, Zhang, Ma, Han, 2006) to solve the problem. Finger-vein feature extraction as an effective scheme has been widely investigated by many researchers and applied for finger-vein verification.

Miura, Nagasaka, and Miyatake (2004) have proposed a repeated line tracking approach to enhance the vein pattern and proved the efficacy of the proposed approach. The performance of finger-vein verification is significantly improved by detecting maximum curvature points in image profiles and the promising experimental results are shown in the Ref. Miura et al. (2007). In Ref. Zhang et al. (2006), a multi-scale feature extraction method of finger-vein patterns based on curvelets and neural networks was investigated and has shown high performance with respect to finger-vein feature extraction. However, the details of the experiment setup are not illustrated in this work. To enhance finger-vein pattern, Yu et al. (2009) have developed an approach to extract concave region by calculating the maximum convolution in eight directions of pixels. Subsequently, they proposed a region growth based approach for finger-vein verification (Qin et al., 2011). To achieve robust feature extraction, in these work (Frangi, Niessen, Vincken, Viergever, 1998, Song, Kim, Kim, Choi, Kong, Lee, 2011, Zhou, Kumar, 2011), the Hessian Phase matrix is employed to enhance the vein pattern. The magnitude of the corresponding eigenvalues of the Hessian matrix reflects the curvature of the principal orientation in the local image region. The vein patterns are extracted by combining the curvature of all orientations. Compared to other methods, these schemes achieve the promising performance. Recently, the Gabor filters are known to achieve the maximum possible joint resolution in spatial and spatial-frequency domain and have been utilized by researchers to enhance finger-vein pattern. The single scale-based Gabor filters (Kumar, Zhou, 2012, Yang, Yang, Shi, 2009, Yang, Yang, Shi, 2009) have been employed for the finger-vein ridge enhancement. However, the accuracy for finger-vein identification is limited because the false veins created by irregular shadowing are emphasized during the enhancement procedure. Therefore, the Gabor filter based approaches are prone to over-segmentation. To overcome the drawback, a multi-scale multiplication operation (Yang & Shi, 2014) is applied to further emphasize vein region and suppress false ridge in the image enhanced by Gabor filters. The experimental results have proved that the proposed method outperforms the existing methods. The multiscale Gabor filters can extract the finger-vein pattern with different scale, but irregular shadowing and noise are still emphasized in their experimental results. For the same purpose, a difference curvature (Qin et al., 2013) is proposed for finger vein verification and show the higher performance than the conventional Gabor filter with respect to vein pattern enhancement and the false vein suppression.

Based on the description of prior work, most finger feature extraction methods aim to detecting ridge/valley region generated by vein pattern. The curvature and Gabor filters as two effective vein detection tools have shown higher accuracy for finger-vein verification. The curvature based approaches (Frangi, Niessen, Vincken, Viergever, 1998, Hashimoto, 2006, Miura, Nagasaka, Miyatake, 2007, Song, Kim, Kim, Choi, Kong, Lee, 2011, Zhou, Kumar, 2011) extract the vein patterns by computing the curvature of the valley. However,the valleys are susceptible to be corrupted by the noise. So, the false vein features are created in the vein image because the curvature is very sensitive to noise. In fact, the other approaches based on detecting valley such as repeated line tracking (Liu, Xie, Yan, Li, Lu, 2013, Miura, Nagasaka, Miyatake, 2004) and region growth (Qin et al., 2011) also suffer from similar problem because the noise can comprise the valley. Gabor filter based methods (Kumar, Zhou, 2012, Yang, Shi, 2014, Yang, Yang, Shi, 2009, Yang, Yang, Shi, 2009) may alleviate the problem because it is more effective to suppress the noises than the curvature based ones. However, these approaches are prone to over-segmentation because it is not easy for them to distinguish vein pattern from the irregular shadowing in a finger-vein image. Therefore, how to extract the vein pattern from a finger-vein image, especially from ambiguous region such as irregular shadowing and noise is still an issue for finger-vein feature extraction.

To extract robust finger-vein patterns, in this paper, we proposed a finger-vein feature extraction approach for verification. Firstly, for given pixel, several patches are determined by rotating a window along different orientations. Secondly, each patch is projected into Radon space by the Radon transform. As the local Radon transform is the collection of integrals along line, it can be treat as a low pass filter. Therefore, the noise can be suppressed in the Radon space. At the same time, the gap between the foreground (vein pattern) and background will be enlarged based on the integration computation. Therefore, the vein patches can create the prominent valleys in Radon space. Thirdly, the vein patterns are extracted based on the curvature of valleys in Radon space. Finally, the finger-vein feature is encoded from enhanced image and the finger-vein verification is achieved by computing the amount of overlap between two finger-vein feature images. Experimental results on two large public databases show the proposed approach can extract the vein patterns from law finger-vein images and significantly improve the accuracy of finger-vein verification.

Currently, few works have employed Radon transform to extract finger-vein pattern for finger-vein identification and quality assessment. In Wu and Ye (2009), a finger-vein image is projected into Radon space. Then, the coefficients of Radon transform image which represent the vein features are input into neural network for identification. A preliminary version of this work was presented in Qin et al. (2012) for quality assessment. The present work has a different motivation and improves the initial version in significant ways. First, in previous works (Qin, Li, Kot, Qin, 2012, Wu, Ye, 2009), a Radon transform is proposed for finger-vein identification and quality assessment. However, in this work, we develop a Radon transform based approach to segment the vein patterns for finger-vein verification. To the best of our knowledge, the Radon transform has not been applied before for finger-vein segmentation. Second, different from the work (Qin et al., 2012), the vary rectangle widow is adapted in the Local Radon transformation and then the difference curvature is computed to enhance the vein patterns. In addition, considerable new analyses and intuitive explanations are added to the proposed approach. Thirdly, we study in this work the effect on matching performance of cropping the testing image. Experimentally, we demonstrate that performance can be improved by eliminating the boundary region of the testing image. Finally, the verification performance of our approach is investigated by carrying the experiments on two public finger-vein databases. Also, we compare with a number of recently published methods and confirm that our model significantly outperforms existing approaches for finger-vein verification.

The rest of this paper is organized as follows: Section 2 presents details on the proposed verification approach. The experimental results and discussion are presented in Section 3. Finally, the key conclusions from this paper are summarized in Section 4.

Section snippets

The proposed approach

Fig. 1 shows the system flow diagram of the proposed finger-vein verification approach. First, a finger-vein image is projected into Radon space by Local Radon transform. Second, the curvature of valley in Radon space is obtained for extracting vein patterns. Thirdly, we compute the difference of curvatures in two perpendicular projection orientations to enhance the vein patterns. Finally, the vein networks are extracted by a binarization approach and further employed for matching.

Experimental results

To estimate the effectiveness and robustness of the proposed method for finger-vein verification, we carried out experiments on the contacted and contactless based databases. The feature extraction methods using maximum curvature (Miura et al., 2007), mean curvature (Song et al., 2011), difference curvature (Qin et al., 2013), Local Binary Patterns (LBP) (Liu, Xie, & Park, 2016), and Sift (Peng, Wang, El-Latif, Li, & Niu, 2012) have been suggested promising results in these literature. The

Conclusions

In this paper, a robust finger-vein image feature extraction approach is proposed for personal verification. Firstly, each patch is projected in to Radon space based on the Radon transform. Secondly, the vein patterns are enhanced by computing the curvature of the valley in the Radon space. Finally, the enhancement images are encoded and matched for verification. The experimental results on two public databases imply that the proposed approach achieves better performance than other promising

Acknowledgments

This work is supported by the National Natural Science Foundation of China (grant nos. 61402063; 51605061), the Natural Science Foundation Project of Chongqing (grant nos. cstc2013kjrc-qnrc40013; cstc2014jcyjA1316), Chongqing Municipal Education Commission Research Project (KJ1400612) and the Scientific Research Foundation of Chongqing Technology and Business University (grant no. 1352019; grant no. 2013-56-04).

References (32)

  • B.N. Huang et al.

    Finger-vein authentication based on wide line detector & pattern normalization

    20th international conference on pattern recognition (ICPR)

    (2010)
  • C. Kauba et al.

    Pre-processing cascades and fusion in finger vein recognition

    BIOSIG

    (2014)
  • M. Kono et al.

    Near-infrared finger vein patterns for personal identification

    Applied Optics

    (2002)
  • A. Kumar et al.

    Human identification using finger images

    IEEE Transactions on Image Processing

    (2012)
  • E.C. Lee et al.

    Restoration method of skin scattering blurred vein image for finger vein recognition

    Electronics Letters

    (2009)
  • T. Liu et al.

    An algorithm for finger-vein segmentation based on modified repeated line tracking

    The Imaging Science Journal

    (2013)
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