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Published
2024-03-22
Section
Articles
How to Cite
FACENET IN DEPTH: A COMPREHENSIVE ANALYSIS OF ITS ADVANTAGES, LIMITATIONS, AND COMPARATIVE PERFORMANCE IN MODERN FACE RECOGNITION TECHNOLOGIES
Ravshan Abduraxmanov Anarbayevich
Javokhir Sherbaev Ravshan ugli
Keywords: Keywords: FaceNet algorithm, Face recognition
Abstract
Annotation: This research paper presents a detailed examination of the FaceNet
algorithm, developed by Google, focusing on its design, operational strengths, and
weaknesses. It outlines the significant advancements FaceNet brings to face
recognition technology, emphasizing its innovative use of deep learning to achieve
remarkable accuracy and efficiency. Furthermore, the paper compares FaceNet with
several other leading algorithms in the domain, such as DeepFace by Facebook,
VGGFace by Visual Geometry Group, and others, across various performance metrics.
Through theoretical evaluation and empirical analysis, this study aims to provide a
holistic view of FaceNet's position in the landscape of face recognition technologies.
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