AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%

Review on Multimodal Biometric

A biometric ,system has been the important affordable and more reliable system .A biometrics identification system is refers to the automatic recognition of individual person based on their characteristics. Early authentication method like which can be stolen or shared by with other person . Biometric has two modals ,unimodals and multimodals .Inunimodol system , it has disadvantages due to lack of its non –versality and unacuptable error rate .To overcome these unimodal issues multimodals is better approach for combining two or more features of person like for sirisdetetmines a authentication (i.e. identification and verification ) this paper that characterstics ,types and biometrics ,fusion levels and research areas etc

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

A Hand-Based Biometric Verification System Using Ant Colony Optimization

This paper presents a novel personal authentication system using hand-based biometrics, which utilizes internal (beneath the skin) structure of veins on the dorsal part of the hand and the outer shape of the hand. The hand-vein and the hand-shape images can be simultaneously acquired by using infrared thermal and digital camera respectively. A claimed identity is authenticated by integrating these two traits based on the score-level fusion in which four fusion rules are used for the integration. Before their fusion, each modality is evaluated individually in terms of error rates and weights are assigned according to their performance. In order to achieve an adaptive security in the proposed bimodal system, an optimal selection of fusion parameters is required. Hence, Ant Colony Optimization (ACO) is employed in the bimodal system to select the weights and also one out of the four fusion rules optimally for the adaptive fusion of the two modalities to meet the user defined security levels. The databases of hand-veins and the hand-shapes consisting of 150 users are acquired using the peg-free imaging setup. The experimental results show genuine acceptance rate (GAR) of 98% at false acceptance rate (FAR) of 0.001% and the system has the potential for any online personal authentication based application.

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Department of Electrical EngineeringBiometrics such as fingerprint, iris, face, and electrocardiogram (ECG) have been investigated as convenient and powerful security tools that can potentially replace or supplement current possession or knowledge based authentication schemes. Recently, multi-spectral skin photomatrix (MSP) has been newly found as one of the biometrics. Moreover, since the interest of usage and security for wearable devices have been increasing, multi-modal biometrics authentication which is combining more than two modalities such as (iris + face) or (iris + fingerprint) for powerful and convenience authentication is widely proposed. However, one practical drawback of biometrics is irrevocability. Unlike password, biometrics can not be canceled and re-used once compromised since they are not changed forever. There have been several works on cancelable biometrics to overcome this drawback. ECG has been investigated as a promising biometrics, but there are few research on cancelable ECG biometrics. As we aim to study a way for multi-modal biometric scheme for wearable devices that is assumed circumstance under some limitations such as relatively high performance, low computing power, and limited information (not sharing users information to the public), in this study, we proposed a multi-modal biometrics authentication by combining ECG and MSP. For investigating the performances versus level of fusions, Adaboost algorithm was studied as a score level fusion method, and Majority Voting was studied as a decision level fusion method. Due to ECG signal is 1 dimensional, it provides benefits in wearable devices for overcoming the computing memory limitation. The reasons that we select MSP combination with ECG are it can be collected by measuring on inner-wrist of human body and it also can be considered as hardly stolen modality in remote ways. For proposed multi-modal biometrics, We evaluate our methods using collected data by Brain-Computer-Interface lab with 63 subjects. Our Adaboost based pro- posed multi modal biometrics method with performance boost yielded 99.7% detection probability at 0.1% false alarm ratio (PD0.1) and 0.3% equal error rate (EER), which are far better than simply combining by Majority Voting algorithm with 21.5% PD0.1 and 1.6% EER. Note that for training the Adaboost algorithm, we used only 9 people dataset which is assumed as public data and not included for testing data set, against for knowledge limitation as the other constraint. As initial step for user template protection, We proposed a cancelable ECG based user authentication using a composite hypothesis testing in compressive sensing do- main by deriving a generalized likelihood ratio test (GLRT) detector. We also pro- posed two performance boost tricks in compressive sensing domain to compensate for performance degradation due to cancelable schemes: user template guided filtering and T-wave shift model based GLRT detector for random projection domain. To verify our proposed method, we investigated cancelable biometrics criteria for the proposed methods to confirm that the proposed algorithms are indeed cancelable. For proposed cancelable ECG authentication, We evaluated our proposed methods using ECG data with 147 subjects from three public ECG data sets (ECG-ID, MIT- BIH Normal / Arrhythmia). Our proposed cancelable ECG authentication method is practically cancelable by satisfying all cancelable biometrics criteria. Moreover, our proposed method with performance boost tricks achieved 97.1% detection probability at 1% false alarm ratio (PD1) and 1.9% equal error rate (EER), which are even better than non-cancelable baseline with 94.4% PD1 and 3.1% EER for single pulse ECG authentication.ope

Fast computation of the performance evaluation of biometric systems: application to multibiometric

The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here