Open AccessShadow Eye: A Security System Featuring Few-Shot Face Recognition with Siamese Networks and Triplet Loss
Author(s) -
Djeddar Afrah,
Abbaci Aymen,
Hakim Bendjenna,
Zeyad Alshaikh
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3615290
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Existing facial recognition systems struggle with scalability and data scarcity, requiring extensive retraining for new users and failing under variable lighting/angles. In this paper, we propose - Shadow Eye - a novel few-shot learning system that achieves 94.2% accuracy on VGGFace2 (surpassing FaceNet by 4.7%) by integrating Siamese Networks with triplet loss and a pre-trained ResNet50 backbone. Unlike existing methods, our proposed Few-Shot Face Recognition (FS-FR) model eliminates retraining for new users - embedding generation from 50-100 images suffices and operates robustly in real-world conditions (pose, lighting, expressions). The Shadow Eye system, powered by the Few-Shot Face Recognition (FS-FR) model, bridges the gap between highly accurate biometric recognition and real-world environments where only limited data is available.
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