Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …

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

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

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

Single image rain streak removal is an extremely challenging problem due to the presence
of non-uniform rain densities in images. We present a novel density-aware multi-stream …

We propose a novel regularization algorithm to train deep neural networks, in which data at
training time is severely biased. Since a neural network efficiently learns data distribution, a …

Abstract Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in
computer vision and biometrics. In this work, we aim to extend LT-ReID beyond pedestrian …

Despite recent advances in face recognition using deep learning, severe accuracy drops are
observed for large pose variations in unconstrained environments. Learning pose-invariant …

This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-
task convolutional neural network (CNN) for face recognition, where identity classification is …

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically
solved by learning a joint embedding space where the semantic content shared between …