This paper presents a comprehensive survey of facial feature point detection with the
assistance of abundant manually labeled images. Facial feature point detection favors many …

Low-rank tensor recovery in the presence of sparse but arbitrary errors is an important
problem with many practical applications. In this work, we propose a general framework that …

This paper studies the problem of simultaneously aligning a batch of linearly correlated
images despite gross corruption (such as occlusion). Our method seeks an optimal set of …

Unsupervised joint alignment of images has been demonstrated to improve performance on
recognition tasks such as face verification. Such alignment reduces undesired variability due …

We present an unsupervised learning framework for decomposing images into layers of
automatically discovered object models. Contrary to recent approaches that model image …

Over the last century, Component Analysis (CA) methods such as Principal Component
Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA) …

In this paper we study the problem of online aligning a newly arrived image to previously
well-aligned images. Inspired by recent advances in batch image alignment using low rank …

Recent advances in image clustering typically focus on learning better deep
representations. In contrast, we present an orthogonal approach that does not rely on …

Time-series analysis is confounded by nonlinear time warping of the data. Traditional
methods for joint alignment do not generalize: after aligning a given signal ensemble, they …

We address the alignment of a group of images with simultaneous registration. Therefore,
we provide further insights into a recently introduced framework for multivariate similarity …