Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

In recent years, researchers have proposed many deep learning (DL) methods for various
tasks, and particularly face recognition (FR) made an enormous leap using these …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains,
catalysing unparalleled enhancements across a broad spectrum of tasks; however, its …

Cross-modality face recognition is an emerging topic due to the wide-spread usage of
different sensors in day-to-day life applications. The development of face recognition …

Iconography in art is the discipline that studies the visual content of artworks to determine
their motifs and themes and to characterize the way these are represented. It is a subject of …

Recently many promising results have been shown on face recognition related problems.
However, age-invariant face recognition and retrieval remains a challenge. Inspired by the …

Facial aging variation is a major problem for face recognition systems due to large intra-
personal variations caused by age progression. A major challenge is to develop an efficient …

Age invariant face recognition (AIFR) is highly required in many applications like law
enforcement, national databases and security. Recognizing faces across aging is difficult …

Abstract Federated Learning with Model Distillation (FedMD) is a nascent collaborative
learning paradigm, where only output logits of public datasets are transmitted as distilled …