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Optical Engineering

On the design of embedded solutions to banknote recognition
Author(s): Adnan Rashid; Andrea Prati; Rita Cucchiara
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Paper Abstract

Banknote recognition systems have many applications in the modern world of automatic monetary transaction machines. They are traditionally based on simple classifiers applied over manually selected areas. A new solution in this field, borrowed by content-based image retrieval (CBIR), which is based on dense scale-invariant feature transform features in a bag-of-words framework followed by a support vector machine (SVM) classifier, is explored. The proposed computer vision system for banknote recognition, on one hand, enables recognition at high accuracy and speed, and, on the other hand, provides basis for further applications, e.g., counterfeit detection and fitness test. This approach makes the system robust to various defects, which may occur during image acquisition or during circulation life of banknote. We implemented and tested on an embedded platform three state-of-the-art classification techniques [SVM, artificial neural network (ANN), and hidden Markov model (HMM)]. The comparative results are reported for accuracy with different sizes of the training datasets and with various types of datasets. In this framework, the SVM classifier outperforms ANN and HMM on the basis of speed and accuracy on our embedded platform.

Paper Details

Date Published: 20 September 2013
PDF: 12 pages
Opt. Eng. 52(9) 093106 doi: 10.1117/1.OE.52.9.093106
Published in: Optical Engineering Volume 52, Issue 9
Show Author Affiliations
Adnan Rashid, Univ. degli Studi di Modena e Reggio Emilia (Italy)
Andrea Prati, Univ. Iuav di Venezia (Italy)
Rita Cucchiara, Univ. degli Studi di Modena e Reggio Emilia (Italy)


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