2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.025
Coin recognition is one of the prime important activities for modern banking and currency processing systems. These systems are widely used for coin sorting, automatic counting, and vending machines. The technique at the heart of such systems is object recognition in a digital image. Object classification and recognition is still one of the challenging research areas because we put our cognitive capabilities in a computer system through an algorithm. The reliability of such systems mainly depends on feature selection and extraction mechanism. This paper presents a novel approach for coins recognition. The proposed method uses Scale Invariant Feature Transform (SIFT) algorithm to handle the issues of rotations, scaling and illumination in a digital image. This is followed by Principle Component Analysis (PCA) for reducing extracted features set. This reduced feature set is passed to feed forward back-propagation artificial neural network (ANN) for classification and recognition. The experimental results indicate that proposed approach achieves state-of-the-art results for Pakistani coin recognition.
Feature extraction, Neural networks, Classification algorithms, Object recognition, Principal component analysis, Gray-scale, Digital images
Ghulam Farooque, Allah Bux Sargano, Imran Shafi, Waqar Ali, "Coin Recognition with Reduced Feature Set SIFT Algorithm Using Neural Network", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 93-98, 2016, doi:10.1109/FIT.2016.025