AN INTELLIGENT CLASSIFIER FUSION TECHNIQUE FOR IMPROVED MULTIMODAL BIOMETRIC AUTHENTICATION USING MODIFIED DEMPSTER-SHAFER RULE OF COMBINATION

Multimodal biometric technology relatively is a technology developed to overcome those limitations imposed by unimodalbiometric systems. The paradigm consolidates evidence from multiple biometric sources offering considerableimprovements in reliability with reasonably overall performance in many applications. Meanwhile, the issue of efficient andeffective information fusion of these evidences obtained from different sources remains an obvious concept that attractsresearch attention. In this research paper, we consider a classical classifier fusion technique, Dempster’s rule of combinationproposed in Dempster-Shafer Theory (DST) of evidence. DST provides useful computational scheme for integratingaccumulative evidences and possesses the potential to update the prior every time a new data is added in the database.However, it has some shortcomings. Dempster Shafer evidence combination has this inability to respond adequately to thefusion of different basic belief assignments (bbas) of evidences, even when the level of conflict between sources is low. Italso has this tendency of completely ignoring plausibility in the measure of its belief. To solve these problems, this paperpresents a modified Dempster’s rule of combination for multimodal biometric authentication which integrates hyperbolictangent (tanh) estimators to overcome the inadequate normalization steps done in the original Dempster’s rule ofcombination. We also adopt a multi-level decision threshold to its measure of belief to model the modified Dempster Shaferrule of combination.Keywords: Information fusion, Multimodal Biometric Authentication, Normalization technique, Tanh Estimators

Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Hyperbolic Face Anti-Spoofing

Learning generalized face anti-spoofing (FAS) models against presentation attacks is essential for the security of face recognition systems. Previous FAS methods usually encourage models to extract discriminative features, of which the distances within the same class (bonafide or attack) are pushed close while those between bonafide and attack are pulled away. However, these methods are designed based on Euclidean distance, which lacks generalization ability for unseen attack detection due to poor hierarchy embedding ability. According to the evidence that different spoofing attacks are intrinsically hierarchical, we propose to learn richer hierarchical and discriminative spoofing cues in hyperbolic space. Specifically, for unimodal FAS learning, the feature embeddings are projected into the Poincar\'e ball, and then the hyperbolic binary logistic regression layer is cascaded for classification. To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks. To alleviate the vanishing gradient problem in hyperbolic space, a new feature clipping method is proposed to enhance the training stability of hyperbolic models. Besides, we further design a multimodal FAS framework with Euclidean multimodal feature decomposition and hyperbolic multimodal feature fusion & classification. Extensive experiments on three benchmark datasets (i.e., WMCA, PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed method can bring significant improvement compared to the Euclidean baselines on unseen attack detection. In addition, the proposed framework is also generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition system can be extremely complex due to high dimension unimodal features in temporal domain. This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. With the objective of reducing the feature vector dimension in the temporal domain, first pre-processing is performed using Contourlet Transform Model. Next, Local Derivative Ternary Pattern model is applied to the pre-processed features where the feature discrimination power is improved by obtaining the coefficients that has maximum variation across pre-processed multimodality features, therefore improving recognition accuracy. Weighted Rank Level Fusion is applied to the extracted multimodal features, that efficiently combine the biometric matching scores from several modalities (i.e. face, fingerprint and iris). Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain. The results of the proposed multimodal biometric recognition framework were compared with other multimodal methods. Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system

Multiple classifiers in biometrics. part 1: Fundamentals and review

We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems. Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance themThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE

Multimodal Biometric Systems for Personal Identification and Authentication using Machine and Deep Learning Classifiers

Multimodal biometrics, using machine and deep learning, has recently gained interest over single biometric modalities. This interest stems from the fact that this technique improves recognition and, thus, provides more security. In fact, by combining the abilities of single biometrics, the fusion of two or more biometric modalities creates a robust recognition system that is resistant to the flaws of individual modalities. However, the excellent recognition of multimodal systems depends on multiple factors, such as the fusion scheme, fusion technique, feature extraction techniques, and classification method. In machine learning, existing works generally use different algorithms for feature extraction of modalities, which makes the system more complex. On the other hand, deep learning, with its ability to extract features automatically, has made recognition more efficient and accurate. Studies deploying deep learning algorithms in multimodal biometric systems tried to find a good compromise between the false acceptance and the false rejection rates (FAR and FRR) to choose the threshold in the matching step. This manual choice is not optimal and depends on the expertise of the solution designer, hence the need to automatize this step. From this perspective, the second part of this thesis details an end-to-end CNN algorithm with an automatic matching mechanism. This thesis has conducted two studies on face and iris multimodal biometric recognition. The first study proposes a new feature extraction technique for biometric systems based on machine learning. The iris and facial features extraction is performed using the Discrete Wavelet Transform (DWT) combined with the Singular Value Decomposition (SVD). Merging the relevant characteristics of the two modalities is used to create a pattern for an individual in the dataset. The experimental results show the robustness of our proposed technique and the efficiency when using the same feature extraction technique for both modalities. The proposed method outperformed the state-of-the-art and gave an accuracy of 98.90%. The second study proposes a deep learning approach using DensNet121 and FaceNet for iris and faces multimodal recognition using feature-level fusion and a new automatic matching technique. The proposed automatic matching approach does not use the threshold to ensure a better compromise between performance and FAR and FRR errors. However, it uses a trained multilayer perceptron (MLP) model that allows people’s automatic classification into two classes: recognized and unrecognized. This platform ensures an accurate and fully automatic process of multimodal recognition. The results obtained by the DenseNet121-FaceNet model by adopting feature-level fusion and automatic matching are very satisfactory. The proposed deep learning models give 99.78% of accuracy, and 99.56% of precision, with 0.22% of FRR and without FAR errors. The proposed and developed platform solutions in this thesis were tested and vali- dated in two different case studies, the central pharmacy of Al-Asria Eye Clinic in Dubai and the Abu Dhabi Police General Headquarters (Police GHQ). The solution allows fast identification of the persons authorized to access the different rooms. It thus protects the pharmacy against any medication abuse and the red zone in the military zone against the unauthorized use of weapons

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above

An adaptive model for multi-modal biometrics decision fusion

Master'sMASTER OF ENGINEERIN