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

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

The performance of face recognition system degrades when the variability of the acquired
faces increases. Prior work alleviates this issue by either monitoring the face quality in pre …

Learning discriminative face features plays a major role in building high-performing face
recognition models. The recent state-of-the-art face recognition solutions proposed to …

This paper tackles the cross-modality person re-identification (re-ID) problem by
suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images …

As an emerging topic in face recognition, designing margin-based loss functions can
increase the feature margin between different classes for enhanced discriminability. More …

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets
and margin-based softmax loss is the current state-of-the-art approach for face recognition …

In the past few years, deep learning-based models have been very successful in achieving
state-of-the-art results in many tasks in computer vision, speech recognition, and natural …

Deep face recognition has achieved great success due to large-scale training databases
and rapidly developing loss functions. The existing algorithms devote to realizing an ideal …