An efficient fingerprint detection system using SVM and neural network (ANN) classifications algorithm.

To be more specific, we are interested in comparing the accuracy of Support Vector Machine with a novel artificial neural network classifier when it comes to fingerprint verification. We compare the new method of artificial neural networks to an SVM. Following evaluating the suggested procedure with a combined total of fifteen samples in both groups, the G power value stands at eighty percent. When it comes to identifying biometric identity, the Support Vector Machine technique only manages 83% accuracy; in contrast, the Novel Artificial Neural Network technique approach achieves an impressive 87% accuracy. With a protest power analysis completed at 85% and test sizes of 15 for one group and 30 for the overall set, the Novel Artificial Neural Network approach outperformed SVM marginally in terms of mean accuracy and standard deviation (1.25369 vs. 1.30700). The Novel Artificial Neural Network Method (1.25369) outperforms the SVM method (1.30700) with a significant value of 0.01 (p<0.05) in terms of mean exactness and standard deviation, although the SVM algorithm (83.10%) ranks somewhat lower. The results demonstrated that when it came to biometric expectancies, the Novel Artificial Neural Network Method Algorithm outperformed the SVM algorithm. [ABSTRACT FROM AUTHOR]