InceptionV3, ResNet50, ResNet18 and MobileNetV2 Performance Comparison on Face Recognition Classification
DOI:
https://doi.org/10.56480/jln.v4i1.990
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Face Recognition, Attendance, CNN, Android, Model ArchitectureAbstract
This research aims to design a facial recognition system for faculty attendance in the Data Science Program at UPN "Veteran" East Java. The study proposes a solution by integrating CNN-based facial recognition technology with Android devices. The methodology involves training CNN models using a dataset of faculty faces and comparing various architectures such as ResNet, MobileNet, and InceptionV3. The research methodology encompasses data collection, data preprocessing, model creation, model comparison, performance evaluation, and implementation. Results from the study, utilizing Convolutional Neural Network models and testing various architectures, reveal the architecture with the best facial recognition performance achieving an average accuracy rate of 77%.
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Copyright (c) 2023 Mohammad Rafka Mahendra Ariefwan, I Gede Susrama Mas Diyasa, Kartika Maulidya Hindrayani
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