Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms

Multimodal Fusion of Biometric Experts.

Person authentication is the process of confirming or determining a person's identity. Its purpose is to ensure that a system can only be accessed by authorised users. The Biometric method uses a person's physical or behavioural characteristics. The use of biometric characteristics is increasingly more popular as it makes unauthorised access more difficult. The use of a single biometric characteristic has some limitations: intra-class variation - differences in the captured biometric characteristics from the same user; lack of distinctiveness - similarities in biometric characteristics from different users; and nonuniversality - not all users being able to provide a particular biometric characteristic. These limitations can be overcome through the use of two or more biometric characteristics. Systems using multiple biometrics give use to the problem of fusion addressed in this thesis. In this thesis two novel methods for quality based fusion are presented. (1) Quality information is included in fusion as a feature to the input of a fusion classifier. This is achieved by weighting similarity measures with the quality measures before fusing the experts. We investigate and compare different ways of including the quality information and present A priori and A posteriori results when combining six face experts and one speech expert. We also present results for all possible combination of experts using a box plot. While current quality dependent fusion algorithms are restricted to the particular fusion classifier or algorithm reported in the literature, our proposed method offers the flexibility of being used with several fusion classifiers. (2) Quality information is used to group data, allowing different parameters/fusion classifiers to be used for each group. A priori and A posteriori results were presented for all possible combination of six face and one speech experts. We also investigated the affect on system performance when the data was split into different numbers of groups. This method utilises the favourable attributes fixed fusion rules. Both quality based fusion methods deliver significant gains in accuracy over combining expert outputs (scores) without quality measures and over other fusion methods using quality information. This thesis also investigates ways of dealing with incomplete samples for fusion. We introduce variants of the popular k-NN imputation method, where we initial predict the class of the sample to ensure that the estimated data is computed only from the predicted class. We also introduced a client specific variant, that allows missing data to only be estimated from the claimed identity of the sample. Both these variants delivered improvement in system accuracy

Multimodal fusion of biometric experts

Person authentication is the process of confirming or determining a person's identity. Its purpose is to ensure that a system can only be accessed by authorised users. The Biometric method uses a person's physical or behavioural characteristics. The use of biometric characteristics is increasingly more popular as it makes unauthorised access more difficult.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Benchmarking Quality-dependent and Cost-sensitive Score-level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework the Biosecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint and iris biometrics for person authentication, targeting the application of physical access control in a mediumsize establishment with some 500 persons. While multimodal biometrics is a well investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependen

Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms

Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms