Humans usually explain their reasoning (eg classification) by dissecting the image and
pointing out the evidence from these parts to the concepts in their minds. Inspired by this …

Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

Abstract The Recurrent Neural Networks and their variants have shown promising
performances in sequence modeling tasks such as Natural Language Processing. These …

In video based face recognition, great success has been made by representing videos as
linear subspaces, which typically lie in a special type of non-Euclidean space known as …

Learning representations on Grassmann manifolds is popular in quite a few visual
recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper …

In this article, we present a general optimization framework that leverages structured sparsity
to achieve superior recovery results. The traditional method for solving the structured sparse …

During the past several years, as one of the most successful applications of sparse coding
and dictionary learning, dictionary-based face recognition has received significant attention …

Deep Learning (DL) has achieved remarkable success in tackling complex Artificial
Intelligence tasks. The standard training of neural networks employs backpropagation to …

This paper introduces a new nonlinear dictionary learning method for histograms in the
probability simplex. The method leverages optimal transport theory, in the sense that our aim …

Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas
of computer vision and machine learning. While these matrices form an open subset of the …