<![CDATA[Face identification is one form of security verification that is currently widely used due to its low level of fraud. Therefore, this study aims to obtain the best model with the method and parameters between CNN and SNN in identifying faces using a dataset sourced from the open-source Kaggle created by Burak from Middle East Technical University, Turkey. The dataset consists of 5 classes, each containing 5 faces with different names but the same number of faces per class. Furthermore, the existing data will undergo a preprocessing process aimed at reducing dimensions and noise in non-uniform data. Next, in this process, the data will be divided into training data and test data for further training using the Tensorflow Keras framework and the visual studio code IDE and Kaggle as a notebook. There are 2 stages of testing in this study, namely testing the performance of the modeling on the system created by using varying epochs and changes in parameters, and testing the performance of the system in performing identification. The model with the best results is the SNN method model, which has a training accuracy of up to 99% and an evaluation result accuracy of up to 98% with a prediction time of only 0.7 seconds. The results of this study indicate that the SNN method has better ability in identifying faces compared to the CNN method. Keywords— Face identification, Machine Learning, Convolutional Neural Network (CNN), Siamese Neural Network (SNN).]]>
Copyrights © 2024
