Palmprint Identification using BPNN, KNN, PNN, RBFN, and RBPNN

This project presents an effective texture-based approach for palmprint recognition. It has three major steps -

• First, region of interest (ROI) is extracted from the hand image.
• Then, features are extracted in the processing stage from the palmprint texture using discrete cosine transform (DCT). Block-wise DCT and Holistic DCT are used to extract features.
• Finally, following machine learning approaches are used for classification -
  • Back-propagation neural network (BPNN)
  • K-nearest neighbor (KNN)
  • Probabilistic neural network (PNN)
  • Radial basis function network (RBFN)
  • Radial basis probabilistic neural network (RBPNN)

Dataset

CASIA Database

Experimental setup

See Reference [1].

Screen shot

Reference

[1] Mrinal Kanti Dhar, Rupak Kanti Dhar, Md. Sanwar Hussain, Mamunul Islam, and Yousha Fatema Rahman, Palmprint Identification Using Radial Basis Probabilistic Neural Network, in: Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 49-53, 2017.

[2] ROI of Palmprint Images (https://www.mathworks.com/matlabcentral/fileexchange/46573-roi-of-palmprint-images), MATLAB Central File Exchange.