Development of a Secure Biometric Recognition System

Muthu, Rajesh (2016) Development of a Secure Biometric Recognition System. Doctoral thesis, Northumbria University.

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Biometric based security systems are becoming an integral part of many security agencies and organisations. These systems have a number of applications ranging from national security, law enforcement, the identification of people, particularly for building access control, the identification of suspects by the police, driver’s licences and many other spheres. However, the main challenge is to ensure the integrity of digital content under different intentional and non-intentional distortions; along with the robustness and security of the digital content.

This thesis focuses on improving the security of fingerprint templates to allow accurate comparison of the fingerprint content. The current methods to generate fingerprint templates for comparison purposes mostly rely on using a single feature extraction technique such as Scale Invariant Feature Transform (SIFT) or Fingerprint Minutiae. However, the combination of two feature extraction techniques (e.g., SIFT-Minutiae) has not been studied in the literature.

This research, therefore, combines the existing feature extraction techniques, SIFTHarris: Feature point detection is critical in image hashing in term of robust feature extraction, SIFT to incorporate the Harris criterion to select most robust feature points and SIFT-Wavelet: Wavelet based technique is basically used to provide more security and reliability of image, SIFT feature with efficient wavelet-based salient points to generate robust SIFT - wavelet feature that provides sufficient invariance to common image manipulations. The above said feature detector are known work well on the natural images (e.g., faces, buildings or shapes) and tests them in the new context of fingerprint images. The results in this thesis demonstrate that new approach contributes towards the improvement of fingerprint template security and accurate fingerprint comparisons.

The fingerprint minutiae extraction method is combined individually with the SIFTHarris method, SIFT-Wavelet method and the SIFT method, to generate the most prominent fingerprint features. These features are post-processed into perceptual hashes using Radial Shape Context Hashing (RSCH) and Angular Shape Context Hashing (ASCH) methods. The accuracy of fingerprint comparison in each case is evaluated using the Receiver Operating Characteristic (ROC) curves.

The experimental results demonstrate that for the JPEG lossy compression and geometric attacks, including rotation and translation, the fingerprint template and accuracy of fingerprint matching improved when combinations of two different Feature extraction techniques are used, in contrast to using only a single feature extraction technique.

The ROC plots illustrates the SIFT-Harris-Minutiae, SIFT-Wavelet-Minutiae, SIFTMinutiae perform better than the SIFT method. The ROC plots further demonstrate that SIFT-Harris-Minutiae outperform all the other techniques. Therefore, SIFTHarris-Minutiae technique is more suitable for generating a template to compare the fingerprint content.

Furthermore, this research focuses on perceptual hashing to improve the minutiae extraction of fingerprint images, even if the fingerprint image has been distorted. The extraction of hash is performed after wavelet transform and singular value decomposition (SVD). The performance evaluation of this approach includes important metrics, such as the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Experimentally, it has confirmed its robustness against image processing operations and geometric attacks.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Fingerprint templates, SIFT, feature extraction, perceptual hashing, shape context
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
University Services > Graduate School > Doctor of Philosophy
Depositing User: Becky Skoyles
Date Deposited: 16 Aug 2017 11:09
Last Modified: 31 Jul 2021 23:08

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