Palm Print Recognition Using CEDA
Shalini Agarwal, Vivek Sharma, Pawan Kumar Verma · 2019
Nowadays, Palm Print is one of the most reliable biometric traits among all of personal identification due to its high stability and various unique as well as stable features like principal lines, minutiae points, singular points, ridges, textures etc. In this paper we present a new method using Curvelet energy distribution algorithm. We extract texture feature from palm image using Curvelet Energy Distribution Algorithm (CEDA), in which First, 2 level curvelet transform is performed over palm image and then energy distribution of each sub-bands will be calculated at both level of transform. These energy distributions will be collected as feature vector, sort them and use as a texture feature of an image. This feature vector generated after sorting does not change for input palm image if one person scans his palm in any angle hence effectively achieve good recognition rate with rotation invariant property. Multi class SVM classifier is used for classification which is a less complex high performance classifier. Experiment will be performed over PolyU palm print database collected over 250 persons. This proposed algorithm compared with recognition using wavelet transform and texture extraction using Gabor filter and achieve a good recognition rate.