Thirimachos Bourlai

The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

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 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 in the literature, 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 biometric 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 the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score.

A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms

This paper presents a test bed, called the Biosecure DS2 score-and-quality database, for evaluating, comparing and benchmarking score-level fusion algorithms for multimodal biometric authentication. It is designed to benchmark quality-dependent, client-specific, cost-sensitive fusion algorithms. A quality-dependent fusion algorithm is one which attempts to devise a fusion strategy that is dependent on the biometric sample quality. A client-specific fusion algorithm, on the other hand, exploits the specific score characteristics of each enrolled user in order to customize the fusion strategy. Finally, a cost-sensitive fusion algorithm attempts to select a subset of biometric modalities/systems (at a specified cost) in order to obtain the maximal generalization performance. To the best of our knowledge, the BioSecure DS2 data set is the first one designed to benchmark the above three aspects of fusion algorithms. This paper contains some baseline experimental results for evaluating the above three types of fusion scenarios.

Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion

As biometric technology is rolled out on a larger scale, it will be a common scenario (known as cross-device matching) to have a template acquired by one biometric device used by another during testing. This requires a biometric system to work with different acquisition devices, an issue known as device interoperability. We further distinguish two subproblems, depending on whether the device identity is known or unknown. In the latter case, we show that the device information can be probabilistically inferred given quality measures (e.g., image resolution) derived from the raw biometric data. By keeping the template unchanged, cross-device matching can result in significant degradation in performance. We propose to minimize this degradation by using device-specific quality-dependent score normalization. In the context of fusion, after having normalized each device output independently, these outputs can be combined using the naive Bayes principal. We have compared and categorized several state-of-the-art quality-based score normalization procedures, depending on how the relationship between quality measures and score is modeled, as follows: 1) direct modeling; 2) modeling via the cluster index of quality measures; and 3) extending 2) to further include the device information (device-specific cluster index). Experimental results carried out on the Biosecure DS2 data set show that the last approach can reduce both false acceptance and false rejection rates simultaneously. Furthermore, the compounded effect of normalizing each system individually in multimodal fusion is a significant improvement in performance over the baseline fusion (without using any quality information) when the device information is given.

Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band

The problem of face verification across the short wave infrared spectrum (SWIR) is studied in order to illustrate the advantages and limitations of SWIR face verification. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of SWIR cross-spectral matching. Experiments also show that images captured under different SWIR wavelengths can be matched to visible images with promising results. The role of multispectral fusion in improving recognition performance in SWIR images is finally illustrated. To the best of our knowledge, this is the first time cross-spectral SWIR face recognition is being investigated in the open literature.

A Study on Using Mid-Wave Infrared Images for Face Recognition

The problem of face identication in the Mid-Wave InfraRed (MWIR) spectrum is studied in order to understand the performance of intra-spectral (MWIR to MWIR) and cross-spectral (visible to MWIR) matching. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of MWIR intra-spectral and cross-spectral matching. Experiments show that images captured in the MWIR band can be eciently matched to MWIR images using existing techniques (originally not designed to address such a problem). These results are comparable to the baseline results, i.e., when comparing visible to visible face images. Experiments also show that cross-spectral matching (the heterogeneous problem, where gallery and probe sets have face images acquired in dierent spectral bands) is a very challenging problem. In order to perform cross-spectral matching, we use multiple texture descriptors and demonstrate that fusing these descriptors improves recognition performance. Experiments on a small database, suggests that the problem of cross-spectral matching requires further investigation.

Improving Biometric Device Interoperability by Likelihood Ratio-based Quality Dependent Score Normalization

As biometric technology is being deployed, it will be a common situation to have a template acquired by one biometric device to be used by another during testing. In our problem formulation, we assume that the device may not be known to the system but can be inferred by a set of automatically derived quality measures. The proposed method of device inference results in a novel device-specific quality-dependent score normalisation called the DS-norm. Experiments based on the BioSecure DS2 data set confirm the benefits and feasibility of the proposed approach.

Design and evaluation of photometric image quality measures for effective face recognition

The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the authors studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.

Restoring Degraded Face Images: A Case Study in Matching Faxed, Printed, and Scanned Photos

We study the problem of restoring severely degraded face images such as images scanned from passport photos or images subjected to fax compression, downscaling, and printing. The purpose of this paper is to illustrate the complexity of face recognition in such realistic scenarios and to provide a viable solution to it. The contributions of this work are two-fold. First, a database of face images is assembled and used to illustrate the challenges associated with matching severely degraded face images. Second, a preprocessing scheme with low computational complexity is developed in order to eliminate the noise present in degraded images and restore their quality. An extensive experimental study is performed to establish that the proposed restoration scheme improves the quality of the ensuing face images while simultaneously improving the performance of face matching.

Multi-spectral face recognition: Identification of people in difficult environments

In this paper we study the problems of intra-spectral and cross-spectral face recognition (FR) in homogeneous and heterogeneous environments. Specifically we investigate the advantages and limitations of matching (i) short wave infrared (SWIR) face images to visible images under controlled or uncontrolled conditions, (ii) mid-wave infrared (MWIR) to MWIR or visible images under controlled conditions, and (iii) intra-distance near infrared (NIR) to NIR images and cross-distance, cross-spectral NIR to visible images. All NIR images were captured night-time, outdoors and at mid-ranges (from 30 up to 120 meters). We utilized both commercial and academic face matchers and performed a set of experiments indicating that our cross-photometric score level fusion rule can be utilized to improve SWIR cross-spectral matching performance across all FR scenarios investigated. We also show that intra-spectral matching results, using either MWIR or NIR images, are comparable to the baseline results, i.e., when comparing visible to visible face images. Our experiments also indicate that the level of improvement in recognition performance is scenario dependent. Experiments also show that cross-spectral matching (the heterogeneous problem, where gallery and probe sets have face images acquired in different spectral bands) is a very challenging problem and it requires further investigation to address real-world law enforcement or military situations.