The contribution of this paper is to compare paradigms coming from the classes of parametric, and non-parametric techniques to solve the decision fusion problem encountered in the design of a multi-modal biometrical identity verification system. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A decision fusion module receiving as input the d scores has to take a binary decision: accept or reject the claimed identity. We have solved this fusion problem using parametric and non-parametric classifiers. The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities. Keywords: Multi-modal identity verification, biometrics, decision fusion. 1 Introduction The automatic verification 1 of a person is more and...

The contribution of this paper is to compare paradigms coming from the classes of parametric, and non-parametric techniques to solve the decision fusion problem encountered in the design of a multi-modal biometrical identity verification system. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A decision fusion module receiving as input the d scores has to take a binary decision: accept or reject the claimed identity. We have solved this fusion problem using parametric and non-parametric classifiers. The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities. Keywords: Multi-modal identity verification, biometrics, decision fusion. 1 Introduction The automatic verification 1 of a person is more and...

CLASSIFICATION

Current text-independent speaker verification systems are usually based on modeling globally the probability density function (PDF) of the speaker feature vectors. In this paper, segmental approaches to text-independent speaker verification are discussed. Unlike the schemes based on Large Vocabulary Continuous Speech Recognition (LVCSR) with previously trained phone models, our systems are based on units derived in unsupervised manner using the ALISP (Automatic Language Independent Processing) tools. Speaker modeling is then done independently for each class of speech sounds. Among the techniques to merge the classdependent scores, linear combination was tested and logistic regression and a method based on the Mixture of Experts technique are under investigation. The experimental results were obtained on the data from the NIST-NSA'98 campaign. Keywords: text-independent speaker verification, segmental approach, data fusion. 1. INTRODUCTION Current text-independent speaker verificatio...

Handwritten text is generally captured through two main modalities: off-line and on-line. Smart approaches to handwritten text recognition (HTR) may take advantage of both modalities if they are available. This is for instance the case in computer-assisted transcription of text images, where on-line text can be used to interactively correct errors made by a main off-line HTR system. We present here baseline results on the biMod-IAM-PRHLT corpus, which was recently compiled for experimentation with techniques aimed at solving the proposed multi-modal HTR problem, and is being used in one of the official ICPR-2010 contests. 1

Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on-line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of text images, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to establish baseline performance figures, we have also obtained uni-modal results for each modality, as well as bi-modal results using Handwritten text is one of the most natural communication channels currently available

Abstract-Multimodal biometric system combines the evidence obtained from multiple modalities. By using an effective fusion scheme and normalization techniques we can significantly improve the over all accuracy and performance of biometric systems. In this paper we have presented information fusion and score normalization approaches that performs better in identification process based on biological features.

For identity related problems, descriptive attributes can take the form of any information that helps represent an individual, including age data, describable visual attributes, and contextual data. With a rich set of descriptive attributes, it is possible to enhance the base matching accuracy of a traditional face identification system through intelligent score weighting. If we can factor any attribute differences between people into our match score calculation, we can deemphasize incorrect results, and ideally lift the correct matching record to a higher rank position. Naturally, the presence of all descriptive attributes during a match instance cannot be expected, especially when considering non-biometric context. Thus, in this paper, we examine the application of Bayesian Attribute Networks to combine descriptive attributes and produce accurate weighting factors to apply to match scores from face recognition systems based on incomplete observations made at match time. We also examine the pragmatic concerns of attribute network creation, and introduce a Noisy-OR formulation for streamlined truth value assignment and more accurate weighting. Experimental results show that incorporating descriptive attributes into the matching process significantly enhances face identification over the baseline by up to 32.8%. 1.