Improving Noisy Face Embeddings Using Time Series Transformers and Adaptive Triplet Loss

Face recognition accuracy is now at the point that it is comparable to human performance, largely driven by improvements in face embedding models. However, face embedding models rely on high-quality images, making them vulnerable to variations in quality, illumination, pose, and occlusion in real-wo...

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Principais autores:	Jednipat Moonrinta, Matthew N. Dailey, Mongkol Ekpanyapong
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2025-01-01
סדרה:	IEEE Access
נושאים:	Face verification transformer encoders adaptive triplet loss sequential face recognition embedding aggregation attention mechanism
גישה מקוונת:	https://ieeexplore.ieee.org/document/11193818/
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!