Abstract

To improve the efficiency in palm print identification based on CNN classifier and KNN classifier. Classification is performed by CNN algorithm (N=25) over KNN algorithm (N=25) for identifying the palm print. CNN is a Machine Learning algorithm which can take an input image, assign importance to various objects in the image and be able to differentiate one from the other. The k- nearest neighbors (KNN) algorithm is a simple, supervised machine learning technique that can be used to solve both the problems that are based on classification and regression. The obtained G-power test value is 80%. By keeping alpha error-threshold by 0.05, enrollment ratio as 0:1, 95% confidence interval, power 80%. The value obtained in terms of accuracy is identified by CNN (95.8%) over KNN (94%). The results were obtained with a significance value of 0.650 (P10.05). The accuracy of palmprint identification in CNN appears to be better than KNN.

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