An Effective Analysis of Palm Print Detection Using SVM over ANN with Improved Accuracy

This study's goal is to assess the efficiency of palmprint recognition using Novel Support Vector Machine (SVM) over Artificial Neural Network (ANN). Materials and Methods: This analysis uses 20 samples from Machine Learning, which is assigned as two groups. Group 1 with 10 samples using Novel Support Vector Machine and group 2 with 10 samples using Artificial Neural Network. For two groups, the sample size was determined using a G-power of 80%.Results: The recognition rate of Novel Support Vector Machine achieves (96.31 %) accuracy and Artificial Neural Network achieves the recognition rate with (87.36%) accuracy. Statistical significance between Novel Support Vector Machine and Artificial Neural Network was assumed at (p>0.05), the insignificant difference value obtained is p=0.46. Conclusion: From artificial intelligence and machine learning, this research concluded that Novel Support Vector Machine has a higher accuracy rate in authentication system detection than Artificial Neural Network.