Palm Print Identification and Classification in the field of Biometric in CNN using KNN

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.