Elsevier

Pattern Recognition

Volume 47, Issue 3, March 2014, Pages 1321-1329
Pattern Recognition

Design of alignment-free cancelable fingerprint templates via curtailed circular convolution

https://doi.org/10.1016/j.patcog.2013.10.003Get rights and content

Highlights

  • Alignment-free cancelable fingerprint templates.

  • Non-invertible transform via curtailed circular convolution.

  • Satisfactory matching performance with low equal error rate (EER).

  • Generated cancelable templates have the properties of non-invertibility, revocability and diversity.

  • Non-disclosure of original fingerprint data.

Abstract

Fraudulent use of stolen fingerprint data and privacy invasion by tracking individuals unlawfully with shared or stolen fingerprint data justify the significance of fingerprint template protection. With no a priori fingerprint image registration, alignment-free cancelable fingerprint templates do not suffer from inaccurate singular point detection. In this paper, we propose an effective alignment-free method for constructing cancelable fingerprint templates via curtailed circular convolution. The proposed method features an efficient one-way transform, which protects the input binary string such that it cannot be retrieved from the length-reduced, convolved output vector. The transformed template fulfills the requirements of non-invertibility, revocability and diversity for cancelable fingerprint templates. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method demonstrates satisfactory performance compared to the existing alignment-free cancelable template schemes.

Introduction

With a long history in forensic and criminal investigations, it is well recognized that fingerprints are the most widely used biometric identifiers. Driven by advances in fingerprint sensing and rapid developments in areas such as image processing and pattern recognition, fingerprint-based biometric systems have ushered in an era of extensive applications in commercial, civilian and financial domains. Fingerprint authentication has been widely deployed in small- and large-scale systems for access control or personal identification.

Due to the intrinsic bond between one's identity and his/her fingerprint and the characteristics of permanence and uniqueness of one's fingerprint, privacy and security concerns arise in the use of fingerprint-based biometric systems. Unlike a compromised token or password, compromised fingerprint data cannot be replaced or reissued. A fingerprint template, if compromised, may leak fingerprint features that can be used to reconstruct a fingerprint image. For example, Cappelli et al. [1] reconstructed a fingerprint image based on a standard template. Feng and Jain [2] developed a method to reconstruct a whole grayscale fingerprint image through the phase image. Wang and Hu [3] proposed a scheme for reconstructing a full fingerprint from a partial fingerprint.

Fraudulent use of stolen fingerprint data and privacy invasion by tracking individuals unlawfully with shared or stolen data make fingerprint template protection absolutely vital. The idea of cancelable biometrics was initiated by Ratha et al. [4], [5]. Cancelable biometrics consist of intentional, repeatable distortions of biometric signals based on transforms that are non-invertible. Cancelable biometrics, also referred to as feature transformations, constitute an important biometric template protection scheme [6]. The well-known work of Ratha et al. [7] is representative of generating cancelable fingerprint templates, in which three non-invertible transformations (cartesian, polar and surface folding) are proposed to transform fingerprint minutiae.

Considering the large variability and uncertainty in fingerprint images, the main challenge in designing cancelable fingerprint templates is to capture discriminatory information while coping with elastic deformation in fingerprint acquisition. Cancelable fingerprint templates are required to possess three properties: pre-image resistance (also known as non-invertibility), revocability and diversity [8]. Non-invertibility implies that it is infeasible or computationally hard to recover original fingerprint features from a transformed, protected template. Revocability ensures that if a stored template is compromised, it can be revoked and a new template can be issued. Diversity allows the generation of different transformed templates from the same fingerprint data, thus preserving users' privacy across different applications.

At the heart of cancelable fingerprint templates is the design of an irreversible transformation. In this paper, we approach such a one-way transform from a fundamental digital signal processing (DSP) perspective. Specifically, our cancelable fingerprint template is built upon the binary bit-string which is generated from quantizing and bin-indexing pair-minutiae vectors. The binary string is treated as a finite-duration input sequence to a linear time-invariant (LTI) system whose impulse response is another finite-duration sequence, controlled by the user-specific key. Recall that conventional system identification (e.g., [9]) and blind channel estimation (e.g., [10], [11]) necessitate invertibility and that in blind channel identification, it is required to have enough samples in the received signal so that source symbols are identifiable. Motivated by this, instead of convolving the above-mentioned two finite-duration sequences to get the system output as in normal convolution operation, we apply the curtailed circular convolution to reduce the data record in the output sequence. Because the output cannot be restored to full length, the input binary string is protected in the sense that it cannot be recovered from the length-reduced output sequence. The shortened, convolved output vector, as a result of the curtailed circular convolution, is taken as the transformed template, which turns out to meet the requirements of non-invertibility, revocability and diversity for cancelable fingerprint templates.

Alignment-free, efficient one-way transform and satisfactory performance are the highlighted strengths of the proposed cancelable fingerprint template design. First, we construct transformed templates based on pair-minutiae vectors and thus relinquish the process of registering fingerprint images with respect to singular points (core and delta). Therefore, our alignment-free cancelable template design overcomes the difficulty of singular point detection. Second, the proposed one-way transform is accomplished by the curtailed circular convolution and realized by multiplying the Discrete Fourier Transform (DFT) of two sequences. It is well known that the DFT can be computed efficiently by exploiting fast Fourier Transform (FFT) algorithms. Third, the proposed method demonstrates good matching performance evaluated over three databases (DB1, DB2 and DB3) of FVC2002 [12]. Performance comparison between the new scheme and some existing methods is detailed in Section 4.

The rest of the paper is organized as follows. Section 2 presents related research on registration-free (or alignment-free) cancelable fingerprint templates. Section 3 describes the development of alignment-free cancelable templates through the curtailed circular convolution and qualitatively compares the proposed method with some existing alignment-free cancelable template design. Section 4 demonstrates and analyzes experimental results as well as discusses the security of the proposed method. The conclusion and future work is given in Section 5.

Section snippets

Related work

Generating cancelable fingerprint templates mainly involves registration-based and registration-free methods. Registration-based methods entail accurate detection of singular points, which is hard to achieve in noisy and rotated fingerprint images [13]. A registration error is likely to result in a matching error. Moreover, it is difficult to define the core point in arch and tented-arch fingerprint patterns. Thus, the accuracy of registration-based methods is limited by these issues. Numerous

Alignment-free cancelable fingerprint template design based on curtailed circular convolution

This section is devoted to the development of alignment-free cancelable fingerprint templates via curtailed circular convolution. We first illustrate a fundamental transform analysis of LTI (linear time-invariant) systems from a DSP perspective and then use this analysis as the prelude to the core transformation in the design of cancelable templates. Suppose that we have an arbitrary finite-duration sequence {r(n)} of length ζ. This sequence is applied as an input signal to excite an LTI system

Experimental results and analysis

We have carried out extensive testing to evaluate the proposed cancelable fingerprint template design. Three databases (DB1, DB2 and DB3) of FVC2002 [12] were used in our experiments. Fingerprint images in these databases cover a wide spectrum in terms of quality with FVC2002 DB3 having the lowest quality images. Each database contains 100 fingers with eight impressions available for each finger. We have conducted partial dataset testing as well as full dataset testing. Partial dataset testing

Conclusion

Cancelable biometrics provide enhanced safeguard against privacy and security threats to biometric systems. In this paper we have proposed the design of alignment-free cancelable fingerprint templates via curtailed circular convolution. By quantizing and bin-indexing pair-minutiae vectors, a binary string is generated. The proposed method features a one-way transform, which protects the input binary string in such a way that it cannot be retrieved from the convolved, output vector of shortened

Conflict of interest statment

None declared.

Acknowledgment

The research is sponsored by ARC projects LP110100602, LP100200538, LP100100404 and DP0985838.

Song Wang is a senior lecturer in the Department of Electronic Engineering, La Trobe University, Australia. She obtained her PhD degree from the Department of Electrical and Electronic Engineering, University of Melbourne, Australia. Her research areas are biometric security, blind system identification and wireless communications.

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    Song Wang is a senior lecturer in the Department of Electronic Engineering, La Trobe University, Australia. She obtained her PhD degree from the Department of Electrical and Electronic Engineering, University of Melbourne, Australia. Her research areas are biometric security, blind system identification and wireless communications.

    Jiankun Hu is a full professor of Cyber Security at the School of Engineering and Information Technology, the University of new South Wales at the Australian Defence Force Academy (UNSW@ADFA), Australia. His major research interest is in computer networking and computer security, especially biometric security. He has been awarded six Australia Research Council Grants. He served as Security Symposium Co-Chair for IEEE GLOBECOM '08 and IEEE ICC '09. He was Program Co-Chair of the 2008 International Symposium on Computer Science and its Applications. He served and is serving as an Associate Editor of the following journals: Journal of Network and Computer Applications, Elsevier; Journal of Security and Communication Networks, Wiley; and Journal of Wireless Communication and Mobile Computing, Wiley. He is the leading Guest Editor of a 2009 special issue on biometric security for mobile computing, Journal of Security and Communication Networks, Wiley. He received a Bachelor's degree in Industrial Automation in 1983 from Hunan University, PR China, a PhD degree in engineering in 1993 from the Harbin Institute of Technology, PR China, and a Master's degree for research in computer science and software engineering from Monash University, Australia, in 2000. In 1995 he completed his postdoctoral fellow work in the Department of Electrical and Electronic Engineering, Harbin Shipbuilding College, PR China. He was a research fellow of the Alexander von Humboldt Foundation in the Department of Electrical and Electronic Engineering, Ruhr University, Germany, during 1995–1997. He worked as a research fellow in the Department of Electrical and Electronic Engineering, Delft University of Technology, The Netherlands, in 1997. Before he moved to RMIT University Australia, he was a research fellow in the Department of Electrical and Electronic Engineering, University of Melbourne, Australia.

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