Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition

Denny Hardiyanto (Universitas PGRI Madiun)
Dyah Anggun Sartika (Politeknik Negeri Madiun)
Imam Junaedi (Politeknik Negeri Madiun)
Sukamto (Politeknik Negeri Madiun)

IJoICT (International Journal on Information and Communication Technology)
Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023

In today's digital era, the process of facial recognition has a very big role. Face recognition has benefits for authentication and identification processes. The development of facial recognition research continues to be carried out with the aim of being able to get the right algorithm, more accurate, faster processing, to be able to recognize faces from various angles. In this study, a performance test was performed on the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture, which is one of the deep learning algorithm developments for facial recognition. AlexNet has 8 convolution layers so that it will not leave even the slightest feature of the object. The process of training and testing the system uses the MATLAB programming language. The number of datasets used is 400 image data which is divided into 360 training image data and 40 test image data. The 400 data come from 4 classes of facial images that have been labeled with names and each classes have 100 images. The training process produces an accuracy of 100% and the testing process produces an accuracy of 95%.