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2024 | OriginalPaper | Buchkapitel

Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics

verfasst von : Joseph K. Iype, Shoney Sebastian

Erschienen in: Computational Sciences and Sustainable Technologies

Verlag: Springer Nature Switzerland

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Abstract

In recent years, facial recognition has emerged as a key technological advancement with numerous useful applications in numerous industries. FaceNet, ArcFace, and OpenFace are three widely used techniques for facial identification. In this study, we examined the accuracy, speed, and capacity to manage variations in face expression, illumination, and occlusion of these three approaches over a period of five years, from 2018 to 2023. According to our findings, FaceNet is more accurate than ArcFace and OpenFace, even under difficult circumstances like shifting lighting and facial occlusion. Also, during the previous five years, FaceNet has shown a significant improvement in performance. Even while ArcFace and OpenFace have made significant strides, they still lag behind FaceNet in terms of accuracy. Therefore, based on our findings, we conclude that FaceNet is the most effective method for facial recognition and is well-suited for use in high-stakes applications where accuracy is crucial.

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Titel: Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics
verfasst von: Joseph K. Iype
Shoney Sebastian
Verlag: Springer Nature Switzerland
Buch: Computational Sciences and Sustainable Technologies
Print ISBN: 978-3-031-50992-6

Electronic ISBN: 978-3-031-50993-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-50993-3_18

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