This is a python implementation of Centroid Displacement-based k-Nearest Neigbors Algorithm (CDNN) which is depend on the idea of the paper Robust Biometric Recognition From Palm Depth Images for Gloved Hands.
The repositry includes:
- CDNN implementation by using numpy
- Examples of using CDNN
- A comparision between CDNN and tradditional k-NN algorithm on some sample datasets
To run the test, install all modules in requirement.txt and run:
python test.py
A sample result will look like this:
Testing with k = 20
---------------Digits dataset------------------
Loading data.....
Done loading data!
Number of classes: 10
Data dimension: 64
Number of training samples: 1437
Number of testing samples: 360
Predict time for CDNN: 3.553s
Accuracy for CDNN with k = 20: 0.967
Predict time for kNN with uniform weights: 0.085s
Accuracy for kNN with k = 20 and uniform weights: 0.964
Predict time for kNN with distance weights: 0.062s
Accuracy for kNN with k = 20 and distance weights: 0.964
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If you use this code or CDNN algorithm for your research, please cite this paper.
@article{nguyen2015robust,
title={Robust biometric recognition from palm depth images for gloved hands},
author={Nguyen, Binh P and Tay, Wei-Liang and Chui, Chee-Kong},
journal={IEEE Transactions on Human-Machine Systems},
volume={45},
number={6},
pages={799--804},
year={2015},
publisher={IEEE}
}