ARTriViT: Automatic Face Recognition System Using ViT-Based Siamese Neural Networks with a Triplet Loss
Document Type
Conference Proceeding
Publication Title
IEEE International Symposium on Industrial Electronics
Abstract
Computer-based face recognition and other biometric techniques are now mature and trustworthy technology that plays a crucial role in many access control scenarios. Face recognition undergoes a variety of difficulties, including those related to angle, lighting, position, facial expression, noise, resolution, occlusion, and the scarcity of samples from each class. In this study, we proposed a triplet loss-based Siamese network with a vision transformer as a feature extractor instead of traditional convolution. Our Siamese analyzes a pair of face images as input, extracts the characteristics from these pairs, and uses similarity indexes to evaluate them for face recognition using the Celeb-DF (version 2) dataset. As a result, the suggested model performs well compared to the state-of-the-art (SOTA) on the Celeb-DF version 2 dataset. The trained model and code will be available at: https://github.com/MuhammadSaeedMBZUAINiTBased-Siamese.
DOI
10.1109/ISIE51358.2023.10228106
Publication Date
8-31-2023
Keywords
Face Recognition, Siamese Neural Network, Triplet Loss, Vision Transformers
Recommended Citation
M. Khan et al., "ARTriViT: Automatic Face Recognition System Using ViT-Based Siamese Neural Networks with a Triplet Loss," IEEE International Symposium on Industrial Electronics, vol. 2023-June, Aug 2023.
The definitive version is available at https://doi.org/10.1109/ISIE51358.2023.10228106
Comments
IR conditions: non-described