Deep rival penalized competitive learning for low-resolution face recognition
P Li, S Tu, L Xu - Neural Networks, 2022 - Elsevier
Current face recognition tasks are usually carried out on high-quality face images, but in
reality, most face images are captured under unconstrained or poor conditions, eg, by video …
reality, most face images are captured under unconstrained or poor conditions, eg, by video …
Derivenet for (very) low resolution image classification
Images captured from a distance often result in (very) low resolution (VLR/LR) region of
interest, requiring automated identification. VLR/LR images (or regions of interest) often …
interest, requiring automated identification. VLR/LR images (or regions of interest) often …
E-ComSupResNet: Enhanced face super-resolution through compact network
Practical systems such as in surveillance applications capture Low-Resolution (LR) face
images due to the wider angle of imaging or longer stand-off distance to the camera …
images due to the wider angle of imaging or longer stand-off distance to the camera …
Distillation of an end-to-end oracle for face verification and recognition sensors
Face recognition functions are today exploited through biometric sensors in many
applications, from extended security systems to inclusion devices; deep neural network …
applications, from extended security systems to inclusion devices; deep neural network …
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline
Although deep learning has significantly improved Face Recognition (FR), dramatic
performance deterioration may occur when processing Low Resolution (LR) faces. To …
performance deterioration may occur when processing Low Resolution (LR) faces. To …