An Analysis of Advanced FaceNet Deep Learning Algorithm in Facial Authentication

D. Gayathri () and V. Raghavendran
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D. Gayathri: Department Computer Science, VISTAS, Research Scholar
V. Raghavendran: VISTAS, Assistant Professor, Department of Computer Science

A chapter in Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024), 2024, pp 546-554 from Springer

Abstract: Abstract Emerging Deep-Learning techniques are highly effective face recognition models, primarily due to the ability to learn complex data representations through multiple layers of processing. Convolutional-neural networks (CNNs) has proven to be the cornerstone of face-recognition systems based on deep learning and plays a central role in their success. This paper explores the architecture, feature extraction, face matching through deep learning features, and the enhancement of the Traditional FaceNet algorithm to an advanced FaceNet Deep Learning Algorithm through additional feature recognition like jaw lines and forehead lines can be used to prove optimum results in accuracy, Mean Average Precision, face verification-accuracy Receiver Operating Characteristic (ROC) Curve, Recall, F1 Score, Face Identification Accuracy.

Keywords: Deep-Learning Networks; Traditional FaceNet algorithm; Advanced-FaceNet Algorithm; Metrics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-433-4_41

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