New Multimodal Biometric Systems with Feature-Level and Score-Level Fusions

In recent years, biometric-based authentication systems have become very important in view of their ability to prevent identity theft by identifying an individual with high accuracy and reliability. Multimodal biometric systems have now drawn some attention in view of their ability to provide a performance superior to that provided by the corresponding unimodal biometric systems by utilizing more than one biometric modality. The existing multimodal biometric systems fuse multiple modalities at a single level, such as sensor, feature, score, rank or decision, and no study to fuse the modalities at more than one level that may lead to a further improvement in the performance of multimodal biometric systems, has been hitherto undertaken. In this thesis, multimodal biometric systems, wherein fusions of the modalities are carried out at more than one level, are investigated. In order to improve the performance of multimodal biometric systems over unimodal biometric systems, normalization and weighting of scores from multiple matchers are essential tasks. In view of this, in the first part of the thesis, a number of normalization and weighting techniques under the score level fusion are investigated. Unlike the existing normalization techniques that are based only on the genuine scores, four new techniques based on both the genuine and impostor scores, are proposed. Two weighting techniques that are based on confidence of the scores, are proposed. Extensive experiments are conducted to evaluate the performance of the multimodal biometric system under the score-level fusion (MBS-SL) using the proposed normalization and weighting techniques. The focus of the second part of this thesis is on the development of multimodal biometric systems, wherein fusions of the modalities are carried out at multiple levels. Specifically, two multimodal biometric systems, in which three modalities are used for their fusion both at the feature level and the score level, are proposed. In the first multimodal biometric system, referred to as the multimodal biometric system with feature level and score level (MBS-FSL) fusions, the features of the three modalities are encoded using the binary hash encoding technique. Unlike the existing techniques for feature level fusion that use unencoded features, this encoding technique allows the neighbourhood feature information to be taken into account. The score-level fusion is carried out on the score obtained from the feature-level fusion and the score from the matching module of the modality that has the lowest equal error rate. In the proposed MBS-FSL, the border values of raw features could not participate in the encoding in view 4-connected neighbors not being available. In order to take both the border and non-border information as well as the neighbourhood information into consideration, a second multimodal biometric system, referred to as the multimodal biometric system with modified feature level and score level (MBS-MFSL) fusions, is proposed, wherein both the raw and encoded features are taken into account. In this system, the feature-level fusion is carried out in a manner similar to that for the MBS-FSL system. The score-level fusion is then carried out between the score obtained from the feature-level fusion, the score from the matching module of the modality that was not utilized in the feature-level fusion, and the scores from individual modalities by using their raw features. Extensive experiments are performed to evaluate the performance of the two proposed multimodal biometric systems. The results of these experiments demonstrate that both of the proposed multimodal biometric systems provide performance superior to that provided by the existing multimodal biometric systems in which fusion of modalities is carried out at a single level, namely, the score level. Experimental results also show that, in view of both the border and neighbourhood feature information being considered in the proposed MBS-MFSL system, it provides a performance superior to that provided by MBS-FSL system. The investigation undertaken in this thesis is aimed at advancing the present knowledge in the field of human biometric identification by considering, for the first time, the fusion of the modalities at two levels, namely, the feature and score levels, and it is hoped that the findings of this study would pave the way for further research in the development of new multimodal biometric systems employing fusion of modalities at multiple levels

A new three-stage scheme for fingerprint enhancement and its impact on fingerprint recognition

In order to provide safety and security from fraudulent acts, it is necessary to use a reliable biometric identifier. Fingerprint is considered to be one of most effective biometric identifiers because of its universal characteristics. The recognition rate of identification/verification systems depends to a great extent on the quality of the fingerprint image. In a fingerprint recognition system, there are two main phases: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching using those extracted features to find the correspondence and similarity between the fingerprint images. The low quality of fingerprint images provides false minutiae at the stage of feature extraction and reduces the recognition rate of minutiae-based fingerprint matching systems. Use of enhanced fingerprint images improves the recognition rate but at the expense of a substantially increased complexity. The objective of this research is to develop an efficient and cost-effective scheme for enhancing fingerprint images that can improve minutiae extraction rate as well as effectively improve the recognition rate of a minutiae-based fingerprint matching system. In the first part of this thesis, a novel low-complexity three-stage scheme for the enhancement of fingerprint images is developed. In the first stage of the scheme, a linear diffusion filter driven by an orientation field is designed to enhance the low-quality fingerprint image. The computational complexity is reduced by using a simple gradient-based method for estimating the orientation field and by using a small number of iterations. Although some of the broken ridges in the fingerprint image are partially connected after the first stage, this stage has a limitation of not being able to connect ridges broken with wide creases, and also not being able to recover ridges in the smeared regions. To overcome the shortcomings of the first stage, the fingerprint image obtained after the first-stage enhancement is passed through a compensation filter in the second stage. Although the broken ridges in the enhanced fingerprint image after the second stage are fully connected, the ridges affected by smears are only partially recovered. Hence, the output obtained from the second stage is passed through the third-stage enhancement, which has two phases: short-time Fourier transform (STFT) analysis and enhancement by an angular filter. In the first phase, a Gaussian spectral window is used in order to perform the STFT and this window helps to reduce the blocking effect in the enhanced image. In the second phase, the image obtained from the STFT is passed through an angular filter, which significantly improves the overall quality of the fingerprint image. In the second part of this thesis, the effectiveness and usefulness of the proposed enhancement scheme are examined in fingerprint feature extraction and matching for fingerprint recognition applications. For this purpose, a minutiae extraction algorithm is first applied to extract minutiae from fingerprint images and then a minutia-based matching algorithm is applied to the set of extracted minutiae using a hybrid shape and orientation descriptor in order to find similarity between a pair of fingerprints. Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases chosen from FVC2000, FVC2002 and FVC2004. Simulation results demonstrate not only the effectiveness of the proposed enhancement scheme in improving the subjective and objective qualities of fingerprint images, but also a superior minutiae extraction rate and a recognition accuracy of the fingerprint images enhanced by the proposed scheme at a reduced computational complexity

A Multi-Biometric System Based on Feature and Score Level Fusions

In general, the information of multiple biometric modalities is fused at a single level, for example, score level or feature level. The recognition accuracy of a multimodal biometric system may not be improved by carrying fusion at a single level, since one matcher may provide a performance lower than that provided by other matchers. In view of this, we propose a new fusion scheme, referred to as the matcher performance-based (MPb) fusion scheme, in which the fusion is carried out at two levels, feature level, and score level, to improve the overall recognition accuracy. First, we consider the performance of the individual matchers in order to find out which of the modalities should be used for fusion at the feature level. Then, the selected modalities are fused at this level by utilizing their encoded features. Next, we fuse the score obtained from the feature-level fusion with that of the modality for which the performance is the highest. In order to carry out this fusion, a new normalization technique referred to as the overlap extrema-variation-based anchored min-max (OEVBAMM) normalization technique, is also proposed. By considering three modalities, namely, fingerprint, palmprint, and earprint, the performance of the proposed fusion scheme as well as that of the single level fusion scheme, both with various normalization and weighting techniques are evaluated in terms of a number of metrics. It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method