Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 86572 9 pages doi 2007 86572 Research Article Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems Krzysztof Kryszczuk Jonas Richiardi Plamen Prodanov and Andrzej Drygajlo Signal Processing Institute Swiss Federal Institute of Technology 1015 Lausanne Switzerland Received 18 May 2006 Revised 1 February 2007 Accepted 31 March 2007 Recommended by Hugo Van Hamme We present a methodology of reliability estimation in the multimodal biometric verification scenario. Reliability estimation has shown to be an efficient and accurate way of predicting and correcting erroneous classification decisions in both unimodal speech face online signature and multimodal speech and face systems. While the initial research results indicate the high potential of the proposed methodology the performance of the reliability estimation in a multimodal setting has not been sufficiently studied or evaluated. In this paper we demonstrate the advantages of using the unimodal reliability information in order to perform an efficient biometric fusion of two modalities. We further show the presented method to be superior to state-of-the-art multimodal decision-level fusion schemes. The experimental evaluation presented in this paper is based on the popular benchmarking bimodal BANCA database. Copyright 2007 Krzysztof Kryszczuk et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Biometric verification systems deployed in a real-world environment often have to contend with adverse conditions of biometric signal acquisition which can be very different from the carefully controlled enrollment conditions. Examples of such conditions include additive acoustic noise that may contaminate the .
