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

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

Multimodal score-level fusion using hybrid ga-pso for multibiometric system

Due to the limitations that unimodal systems suffer from, Multibiometric systems have gained much interest in the research community on the grounds that they alleviate most of these limitations and are capable of producing better accuracies and performances. One of the important steps to reach this is the choice of the fusion techniques utilized. In this paper, a modeling step based on a hybrid algorithm, that includes Particle Swarm Optimization and Genetic Algorithm, is proposed to combine two biometric modalities at the score level. This optimization technique is employed to find the optimum weights associated to the modalities being fused. An analysis of the results is carried out on the basis of comparing the EER accuracies and ROC curves of the fusion techniques. Furthermore, the execution speed of the hybrid approach is discussed and compared to that of the single optimization algorithms, GA and PS

Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model

This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno's fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Genetic And Evolutionary Biometrics:Multiobjective, Multimodal, Feature Selection/Weighting For Tightly Coupled Periocular And Face Recognition

The Genetic & Evolutionary Computation (GEC) research community has seen the emergence of a new subarea, referred to as Genetic & Evolutionary Biometrics (GEB), as GECs have been applied to solve a variety of biometric problems. In this dissertation, we present three new GEB techniques for multibiometric recognition: Genetic & Evolutionary Feature Selection (GEFeS), Weighting (GEFeW), and Weighting/Selection (GEFeWS). Instead of selecting the most salient individual features, these techniques evolve subsets of the most salient combinations of features and/or weight features based on their discriminative ability in an effort to increase accuracy while decreasing the overall number of features needed for recognition. We also incorporate cross validation into our best performing technique in an attempt to evolve feature masks (FMs) that also generalize well to unseen subjects and we search the value preference space in an attempt to analyze its impact in respect to optimization and generalization. Our results show that by fusing the periocular biometric with the face, we can achieve higher recognition accuracies than using the two biometric modalities independently. Our results also show that our GEB techniques are able to achieve higher recognition rates than the baseline methods, while using significantly fewer features. In addition, by incorporating machine learning, we were able to create FMs that also generalize well to unseen subjects and use less than 50% of the extracted features. Finally, by searching the value preference space, we were able to determine which weights were most effective in terms of optimization and generalization

A survey of wearable biometric recognition systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY) and the CAM Grant S2013/ICE-3095 (CIBERDINE

A Survey of Wearable Biometric Recognition Systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work

Multimodal biometric authentication algorithm at score level fusion using hybrid optimization

Biometric is emerging technology in identification and authentication of human being with more reliable and accurate. Combining multiple biometric systems is a promising solution to provide more security. It eliminates the disadvantages of unimodal biometric systems such as non-universality, noise in sensed data, intra-class variations, distinctiveness, spoof attacks and traditional method of authenticating a human and their identity. The proposed method depicts a multimodal biometric algorithm is designed to recognize individuals for robust and secured authentication using normalized score level fusion techniques with hybrid Genetic Algorithm and Particle Swarm Optimization for optimization in order to reduce False Acceptance Rate and False Rejection Rate and to enhance Equal Error Rate and Accuracy.