AN APPROACH TO HUMAN FACE RECOGNITION BY MACHINE LEARNING TRAINING
Main Article Content
Abstract
Facial recognition is a biometric technology technique that faces human facial features. The Facial Expression Recognition 2013(FER-2013) image dataset, including seven different types of human facial expressions, was used as the training dataset in this study. The importance of system security is urgent to deploy a human face recognition authentication application to log in to the system, authenticate on smartphones, and time attendance. We propose a machine learning, deep learning model with many different training methods combined with the FER-2013 dataset, which is expanded with image sizes (32x32, 48x48, 64x64, 72x72) to conduct experiments with LDA, NB, KNN, DT, and SVM models. Then, evaluating the effectiveness of each model in terms of accuracy, precision, and F1-Score was conducted. The experimental results have three main contributions: (a)expanding the dataset format with more diverse sizes; (b) simulating different algorithmic models during training to evaluate and compare the above criteria, and (c) showing the effectiveness of the proposed CNN deep learning model.
Keywords
computer vision, deep learning, face recognition, FER-2013, neural networks
Article Details
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