Credence estimation and error prediction in biometric identity verification

This paper focuses on the estimation of credence in the correctness of classification decisions produced by a biometric identity verification system. We adopt the concept of decision credence defined in terms of subjective Bayesian degree of belief. We demonstrate how credence estimates can be used to predict verification errors and to rectify them, thus improving the classification performance. We also show how the framework of credence estimation helps handle erroneous classification decisions thanks to seamless incorporation of quality measures. Further, we demonstrate that credence information can be effectively applied to perform fusion of decisions in a multimodal scenario

Handling high dimensionality in biometric classification with multiple quality measures using Locality Preserving Projection

The use of quality measures in biometrics is rapidly becoming the standard strategy for improving performance of biometric systems, especially in the presence of variable environmental conditions of signal capture. It is often necessary to integrate multiple quality measures into the classification process in order to capture the relevant aspects of signal quality. The inclusion of multiple quality features quickly increases the dimensionality of the classification problem, which leads to the risks of overfitting and dimensionality curse. So far, no mature strategy of coping with multiple quality measures has been developed. In this paper we propose to use a scheme, where the dimensionality of the vector of quality measures is reduced using the Locality Preserving Projections. We show that the proposed technique offers higher accuracy and better generalization properties than existing techniques of classification with quality measures, in same- and cross-device biometric matching scenarios

On quality of quality measures for classification

Abstract. In this paper we provide a theoretical discussion of the impact of uncertainty in quality measurement on the expected benefits of including biometric signal quality measures in classification. While an ideal signal quality measure should be a precise quantification of the actual signal properties relevant to the classification process, a real quality measurement may be uncertain. We show how does the degree of uncertainty in quality measurement impact the gains in class separation achieved thanks to using quality measures as conditionally relevant classification feature. We demonstrate that while noisy quality measures become irrelevant classification features, they do not impair class separation beyond the baseline result. We present supporting experimental results using synthetic data. Key words: quality measures, feature relevance, classifier ensembles

Quality dependent fusion of intramodal and multimodal biometric experts - art. no. 653903

We address the problem of score level fusion of intramodal and multimodal experts in the context of biometric identity verification. We investigate the merits of confidence based weighting of component experts. In contrast to the conventional approach where confidence values are derived from scores, we use instead raw measures of biometric data quality to control the influence of each expert on the final fused score. We show that quality based fusion gives better performance than quality free fusion. The use of quality weighted scores as features in the definition of the fusion functions leads to further improvements. We demonstrate that the achievable performance gain is also affected by the choice of fusion architecture. The evaluation of the proposed methodology involves 6 face and one speech verification experts. It is carried out on the XM2VTS data, base

Score Level Fusion Scheme in Hybrid Multibiometric System

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Face Authentication Competition on the BANCA Database

This paper details the results of a face verification competition [2] held in conjunction with the First International Conference on Biometric Authentication. The contest was held on the publically available BANCA database [1] according to a defined protocol [6]. Six different verification algorithms from 4 academic and commercial institutions submitted results. Also, a standard set of face recognition software from the internet [3] was used to provide a baseline performance measure