Hybrid Data Storage Framework for the Biometrics Domain

Biometric based authentication is one of the most popular techniques adopted in large-scale identity matching systems due to its robustness in access control. In recent years, the number of enrolments has increased significantly posing serious issues towards the performance and scalability of these systems. In addition, the use of multiple modalities (such as face, iris and fingerprint) is further increasing the issues related to scalability. This research work focuses on the development of a new Hybrid Data Storage Framework (HDSF) that would improve scalability and performance of biometric authentication systems (BAS). In this framework, the scalability issue is addressed by integrating relational database and NoSQL data store, which combines the strengths of both. The proposed framework improves the performance of BAS in three areas (i) by proposing a new biographic match score based key filtering process, to identify any duplicate records in the storage (de-duplication search); (ii) by proposing a multi-modal biometric index based key filtering process for identification and de-duplication search operations; (iii) by adopting parallel biometric matching approach for identification, enrolment and verification processes. The efficacy of the proposed framework is compared with that of the traditional BAS and on several values of False Rejection Rate (FRR). Using our dataset and algorithms it is observed that when compared to traditional BAS, the HDSF is able to show an overall efficiency improvement of more than 54% for zero FRR and above 60% for FRR values between 1-3.5% during identification search operations

A Novel Convolutional Neural Network Pore-Based Fingerprint Recognition System

Biometrics play an important role in security measures, such as border control and online transactions, relying on traits like uniqueness and permanence. Among the different biometrics, the fingerprint stands out for their enduring nature and individual uniqueness. Fingerprint recognition systems traditionally rely on ridge patterns (Level 1) and minutiae (Level 2). However, these systems suffer from recognition accuracy with partial fingerprints. Level 3 features, such as pores, offer distinctive attributes crucial for individual identification, particularly with high-resolution acquisition devices. Moreover, the use of convolutional neural networks (CNNs) has significantly improved the accuracy in automatic feature extraction for biometric recognition. A CNN-based pore fingerprint recognition system consists of two main modules, pore detection and pore feature extraction and matching modules. The first module generates pixel intensity maps to determine the pore centroids, while the second module extracts relevant features of pores to generate pore representations for matching between query and template fingerprints. However, existing CNN architectures lack in generating deep-level discriminative feature and computational efficiency. Moreover, available knowledge on the pores has not been taken into consideration optimally for pore centroids and metrics other than Euclidean distance have not been explored for pore matching. The objective of this research is to develop a CNN-based pore fingerprint recognition scheme that is capable of providing a low-complexity and high-accuracy performance. The design of the CNN architecture of the two modules aimed at generating features at different hierarchical levels in residual frameworks and fusing them to produce comprehensive sets of discriminative features. Depthwise and depthwise separable convolution operations are judiciously used to keep the complexity of networks low. In the proposed pore centroid part, the knowledge of the variation of the pore characteristics is used. In the proposed pore matching scheme, a composite metric, encompassing the Euclidean distance, angle, and magnitudes difference between the vectors of pore representations, is proposed to measure the similarity between the pores in the query and template images. Extensive experiments are performed on fingerprint images from the benchmark PolyU High-Resolution-Fingerprint dataset to demonstrate the effectiveness of the various strategies developed and used in the proposed scheme for fingerprint recognition

Fingerprint Recognition: A Histogram Analysis Based Fuzzy C-Means Multilevel Structural Approach

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Fingerprints are considered one of the best biometric measurements and are used as a universal personal identifier. There are two main phases in the recognition of personal identity using fingerprints: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching making use of the extracted features to find the correspondence and similarity between the fingerprint images. Use of global features in minutia-based fingerprint recognition schemes enhances their recognition capability but at the expense of a substantially increased complexity. The recognition accuracies of most of the fingerprint recognition schemes, which rely on some sort of crisp clustering of the fingerprint features, are adversely affected due to the problems associated with the behavioral and anatomical characteristics of the fingerprints. The objective of this research is to develop efficient and cost-effective techniques for fingerprint recognition, that can meet the challenges arising from using both the local and global features of the fingerprints as well as effectively deal with the problems resulting from the crisp clustering of the fingerprint features. To this end, the structural information of local and global features of fingerprints are used for their decomposition, representation and matching in a multilevel hierarchical framework. The problems associated with the crisp clustering of the fingerprint features are addressed by incorporating the ideas of fuzzy logic in developing the various stages of the proposed fingerprint recognition scheme. In the first part of this thesis, a novel low-complexity multilevel structural scheme for fingerprint recognition (MSFR) is proposed by first decomposing fingerprint images into regions based on crisp partitioning of some global features of the fingerprints. Then, multilevel feature vectors representing the structural information of the fingerprints are formulated by employing both the global and local features, and a fast multilevel matching algorithm using this representation is devised. Inspired by the ability of fuzzy-based clustering techniques in dealing more effectively with the natural patterns, in the second part of the thesis, a new fuzzy based clustering technique that can deal with the partitioning problem of the fingerprint having the behavioral and anatomical characteristics is proposed and then used to develop a fuzzy based multilevel structural fingerprint recognition scheme. First, a histogram analysis fuzzy c-means (HA-FCM) clustering technique is devised for the partitioning of the fingerprints. The parameters of this partitioning technique, i.e., the number of clusters and the set of initial cluster centers, are determined in an automated manner by employing the histogram of the fingerprint orientation field. The development of the HA-FCM partitioning scheme is further pursued to devise an enhanced HA-FCM (EAH-FCM) algorithm. In this algorithm, the smoothness of the fingerprint partitioning is improved through a regularization of the fingerprint orientation field, and the computational complexity is reduced by decreasing the number of operations and by increasing the convergence rate of the underlying iterative process of the HA-FCM technique. Finally, a new fuzzy based fingerprint recognition scheme (FMSFR), based on the EHA-FCM partitioning scheme and the basic ideas used in the development of the MSFR scheme, is proposed. Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases. These databases are selected from the FVC2002, FVC2004 and FVC2006 competitions containing a wide variety of challenges for fingerprint recognition. Simulation results demonstrate not only the effectiveness of the proposed techniques and schemes but also their superiority over some of the state-of-the-art techniques, in terms of the recognition accuracy and the computational complexity