Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics
Accuracy; ArcFace; Comparison; FaceNet; Facial expression; Facial recognition; High stakes applications; Improvement; Lighting; Occlusion; OpenFace; Performance; Speed
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. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Communications in Computer and Information Science, Vol-1973 CCIS, pp. 222-234.
Springer Science and Business Media Deutschland GmbH
Iype J.K., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560001, India; Sebastian S., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560001, India