In this work we focus on learning facial representations that can be adapted to train effective
face recognition models particularly in the absence of labels. Firstly compared with existing …

We propose a novel approach for individual recognition that uses the motion traits from face
outline-anonymised videos as the identity signatures; we call this type of signature a …

Retinal vascular images contain rich vascular geometric features, so the accuracy of retinal
vascular segmentation is of great significance for the diagnosis of various eye diseases. In …

Traditional face recognition algorithms rely on margin-based softmax loss functions merely.
However, these algorithms tend to perform poorly with low-quality images owing to the …

Iris and Periocular Recognition using Deep Learning systematically explains the
fundamental and most advanced techniques for ocular imprint-based human identification …

Foundational models, trained on vast and diverse datasets, have demonstrated remarkable
capabilities in generalizing across different domains and distributions for various zero-shot …

Traditional face recognition (FR) algorithms often rely merely on margin-based softmax loss
functions. However, due to noisy training data and varied image quality in datasets, these …