InceptionV3, ResNet50, ResNet18 and MobileNetV2 Performance Comparison on Face Recognition Classification

Mohammad Rafka Mahendra Ariefwan; I Gede Susrama Mas Diyasa; Kartika Maulidya Hindrayani

doi:10.56480/jln.v4i1.990

InceptionV3, ResNet50, ResNet18 and MobileNetV2 Performance Comparison on Face Recognition Classification

Authors

Mohammad Rafka Mahendra Ariefwan Program Studi Sains Data, Fakultas Ilmu Komputer, UPN “Veteran” Jawa Timur, Surabaya, Indonesia
I Gede Susrama Mas Diyasa Program Studi Magister Teknologi Informasi, Fakultas Ilmu Komputer, UPN “Veteran” Jawa Timur, Surabaya, Indonesia
Kartika Maulidya Hindrayani Program Studi Sains Data, Fakultas Ilmu Komputer, UPN “Veteran” Jawa Timur, Surabaya, Indonesia

DOI:

https://doi.org/10.56480/jln.v4i1.990

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338

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280

Keywords:

Face Recognition, Attendance, CNN, Android, Model Architecture

Abstract

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%.

References

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Published

11/01/2023

How to Cite

Ariefwan, M. R. M., Diyasa, I. G. S. M., & Hindrayani, K. M. (2023). InceptionV3, ResNet50, ResNet18 and MobileNetV2 Performance Comparison on Face Recognition Classification. Literasi Nusantara, 4(1), 1–10. https://doi.org/10.56480/jln.v4i1.990

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Vol. 4 No. 1 (2023): Literasi Nusantara: November 2023- February 2024

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Articles

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