A system that combines real-time face tracking as well as the localization of facial landmarks in order to improve the authenticity of fingerprint recognition is introduced. The intended purpose of this application is to assist in securing public areas and individuals, in addition to enforce that the collected sensor data in a multi modal person authentication system originate from present persons, i.e. the system is not under a so called play back attack. Facial features are extracted with the help of Gabor filters and classified by SVM experts. For real-time performance, selected points from a retinotopic grid are used to form regional face models. Additionally only a subset of the Gabor decomposition is used for different face regions. The second modality presented is texture-based fingerprint recognition, exploiting linear symmetry. Experimental results on the proposed system are presented. Keywords--- face tracking; Multi modal person authenticaion; Biometric identity authentication; Support Vector Machines; Gabor decomposition; Log-polar sampling; Fingerprint recognition.

Abstract. This study investigates a new confidence criterion to improve fusion via a linear combination of scores of several biometric authentication systems. This confidence is based on the margin of making a decision, which answers the question, “after observing the score of a given system, what is the confidence (or risk) associated to that given access?”. In the context of multimodal and intramodal fusion, such information proves valuable because the margin information can determine which of the systems should be given higher weights. Finally, we propose a linear discriminative framework to fuse the margin information with an existing global fusion function. The results of 32 fusion experiments carried out on the XM2VTS multimodal database show that fusion using margin (product of margin and expert opinion) is superior over fusion without the margin information (i.e., the original expert opinion). Furthermore, combining both sources of information increases fusion performance further. 1

A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-o# coe#cients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.

The use of quality information on automatic recognition systems is studied. From an apparent definition of what constitutes a quality measure, a framework for the successful exploitation of the quality information is derived. Potential applications are also introduced at different phases of the recognition process, namely: enrollment, scoring and multi-level fusion stages. Traditional likelihood scoring stage is further developed providing guidelines for the practical application of the proposed ideas. Preliminary experiments corroborate the benefits of the proposed quality-guided recognition approach. In particular, a frame-level quality measure meeting a goodness criterion based on deviation from the fundamental frequency is used, obtaining encouraging initial results.

Face verification is a difficult classification problem due to the fact that the appearance of a face can be altered by many extraneous factors, including head pose, illumination conditions, etc. A face verification system is likely to produce erroneous, unreliable decisions if there is a mismatch between the image acquisition conditions during the system training and the testing phases. We propose to detect and discard unreliable decisions based on the evidence originating from the classifier scores- and signal domains. We present a method of combining the reliability evidence, nested in a probabilistic framework that allows high level of flexibility in adding new evidence. Finally, we demonstrate on a standard evaluation database (Banca) how the proposed methodology helps in discarding unreliable decisions in a face verification system. 1.

A method using local features to assess the quality of an image, with demonstration in biometrics, is proposed. Recently, image quality awareness has been found to increase recognition rates and to support decisions in multimodal authentication systems significantly. Nevertheless, automatic quality assessment is still an open issue, especially with regard to general tasks. Indicators of perceptual quality like noise, lack of structure, blur, etc. can be retrieved from the orientation tensor of an image, but there are few studies reporting on this. Here we study the orientation tensor with a set of symmetry descriptors, which can be varied according to the application. Allowed classes of local shapes are generically provided by the user but no training or explicit reference information is required. Experimental results are given for fingerprint. Furthermore, we indicate the applicability of the proposed method to face images. 1.

The use of quality information for multilevel speaker recognition systems is addressed in this contribution. From a definition of what constitutes a quality measure, two applications are proposed at different phases of the recognition process: scoring and multi-level fusion stages. The traditional likelihood scoring stage is further developed providing guidelines for the practical application of the proposed ideas. Conventional user-independent multilevel Support Vector Machine (SVM) score fusion is also adapted for the inclusion of quality information in the fusion process. In particular, quality measures meeting three different goodness criteria: SNR, F0 deviations and the ITUP.563 objective speech quality assessment are used in the speaker recognition process. Experiments carried out in the Switchboard-I database assess the benefits of the proposed quality-guided recognition approach for both the score computation and score fusion stages.

Signal quality awareness has been found to increase recognition rates and to support decisions in multi-sensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here we study the orientation tensor of fingerprint images to quantify signal impairments like noise, lack of structure, blur, with the help of symmetry descriptors. Especially favorable in Biometrics, strongly reduced reference, but not less information is sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ) as well as human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multi-algorithm fingerprint recognition environment. In this study, several trained and non-trained score level fusion schemes are investigated. A Bayes-based strategy for incorporating experts ’ past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, are presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).

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-dependent

Humans are excellent experts in person recognition and yet they do not perform excessively well in recognizing others only based on one modality such as single facial image. Experimental evidence of this fact is reported concluding that even human authentication relies on multimodal signal analysis. The elements of automatic multimodal authentication along with system models are then presented. These include the machine experts as well as machine supervisors. In particular, fingerprint and speech based systems will serve as illustration. A signal adaptive supervisor based on the input biometric signal quality is evaluated.