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Báo cáo hóa học: " Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model"

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: Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model | EURASIP Journal on Applied Signal Processing 2004 4 559-571 2004 Hindawi Publishing Corporation Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model J. Coetzer Department of Applied Mathematics University of Stellenbosch Matieland 7602 South Africa Email jcoetzer@sun.ac.za B. M. Herbst Department of Applied Mathematics University of Stellenbosch Matieland 7602 South Africa Email herbst@ibis.sun.ac.za J. A. du Preez Department of Electrical and Electronic Engineering University of Stellenbosch Matieland 7602 South Africa Email dupreez@dsp.sun.ac.za Received 31 October 2002 Revised 27 June 2003 We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform DRT and a hidden Markov model HMM . Given the robustness of our algorithm and the fact that only global features are considered satisfactory results are obtained. Using a database of 924 signatures from 22 writers our system achieves an equal error rate EER of 18 when only high-quality forgeries skilled forgeries are considered and an EER of 4.5 in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers our system achieves an EER of 12.2 when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features. Keywords and phrases offline signature verification discrete Radon transform hidden Markov model. 1. INTRODUCTION The purpose of our research is to develop a system that automatically classifies handwritten signature images as authentic or fraudulent with as little misclassifications as possible. At the same time the processing requirements must be feasible so as to make the adoption of such an automated system economically viable. Our work is inspired