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

The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

This paper addresses deep face recognition (FR) problem under open-set protocol, where
ideal face features are expected to have smaller maximal intra-class distance than minimal …

The volume of convolutional neural network (CNN) models proposed for face recognition
has been continuously growing larger to better fit the large amount of training data. When …

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

Human faces in surveillance videos often suffer from severe image blur, dramatic pose
variations, and occlusion. In this paper, we propose a comprehensive framework based on …

This paper targets on the problem of set to set recognition, which learns the metric between
two image sets. Images in each set belong to the same identity. Since images in a set can be …

Conventional metric learning methods usually assume that the training and test samples are
captured in similar scenarios so that their distributions are assumed to be the same. This …

Person re-identification (Re-ID) aims to match identities across non-overlap** camera
views. Researchers have proposed many supervised Re-ID models which require quantities …

This paper presents an unsupervised deep-learning framework named local deep-feature
alignment (LDFA) for dimension reduction. We construct neighbourhood for each data …