The goal of this paper is face recognition – from either a single photograph or from a set of
faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to …

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 this survey, we focus on 2D image-based end-to-end deep face recognition which takes …
end-to-end deep face recognition. Specifically, an end-to-end deep face recognition system is …

Face recognition constitutes a relatively a popular area which has emerged from the rulers
of the social media to top universities in the world. Those frontiers and rule makers recently …

Face images appearing in multimedia applications, eg, social networks and digital
entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in …

Convolutional neural networks (CNNs) have been widely used in computer vision community,
significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss …

Robust face representation is imperative to highly accurate face recognition. In this work, we
propose an open source face recognition method with deep representation named as …

Convolutional neural networks have significantly boosted the performance of face recognition
in recent years due to its high capacity in learning discriminative features. In order to …

… Table 1: Training data for deep face recognition. The … , we delve into million-scale deep
face recognition problems. The … the frontiers of deep face recognition: WebFace42M achieves …

Today, computer based face recognition is a mature and reliable mechanism which is being
practically utilised for many access control scenarios. As such, face recognition or …