Banknote Classification Based on Convolutional Neural Network in Quaternion Wavelet Domain

Abstract

In this paper, we propose a new framework for banknote classification based on quaternion wavelet transform (QWT) and deep convolutional neural network. Firstly, the QWT is applied to describe the phase and magnitude of different banknote images which has inherent directional sensitivity and multi-scale framework. Then we design a deep convolutional neural network which is trained on banknote images along with the magnitude and phase of quaternion wavelet coefficients. We assign the neural weights on the output probabilities of deep convolutional neural network and update these weights by utilizing the back propagation algorithm. Finally, the trained networks with decision of a weighted sum and the magnitude and the phase of quaternion wavelet networks are utilized for banknote image classification. The performance of our algorithm is experimentally verified on a variety of banknote databases. Experimental results show that the proposed algorithm achieves superior performance compared with other state-of-the-art banknote classification algorithms. The proposed algorithm can also satisfy the real-time requirements of the banknote sorting system.

Keywords

• Quaternion wavelet transform
• convolutional neural network
• Banknote classification
• mixture model