Implementation of Multiple Kernel Support Vector Machine for Automatic Recognition and Classification of Counterfeit Notes
Sharmishta Suhas Desai, Shraddha Kabade, Apurva Bakshi, Apeksha Gunjal, Meghana Yeole · 2014
With the advance of digital imaging technologies, color scanners and laser printers make it increasingly easier to produce counterfeit bank- notes with high resolution.Almost every country in the world face the problem of counterfeit currency notes.Even receiving Fake notes from ATM coun- ters,vending machines and during elections have also been reported at some places.There is a need to design a system that is helpful in recognition of counterfeit notes. In this paper, we propose a system based on multiple-kernel support vector machines for counterfeit banknote recognition. Each banknote is divided into partitions and the luminance histograms of the partitions are taken as the input of the system. Linearly weighted combination is adopted to combine multiple kernels into a combined matrix. Two strategies are adopted to reduce the amount of time and space required by the semi- definite programming (SDP) method. One strategy assumes the non-negativity of the kernel weights, and the other one is to set the sum of the weights to be unity.