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

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 …

Face recognition capabilities have recently made extraordinary leaps. Though this progress
is at least partially due to ballooning training set sizes–huge numbers of face images …

We propose a method to push the frontiers of unconstrained face recognition in the wild,
focusing on the problem of extreme pose variations. As opposed to current techniques which …

We describe a deep learning based method for estimating 3D facial expression coefficients.
Unlike previous work, our process does not relay on facial landmark detection methods as a …

We propose a method designed to push the frontiers of unconstrained face recognition in
the wild with an emphasis on extreme out-of-plane pose variations. Existing methods either …

We present a novel method for modeling 3D face shape, viewpoint, and expression from a
single, unconstrained photo. Our method uses three deep convolutional neural networks to …

In this paper, we propose a new and effective frontalization algorithm for frontal rendering of
unconstrained face images, and experiment it for face recognition. Initially, a 3DMM is fit to …

Face analysis from 2D images and videos is a central task in many multimedia applications.
Methods developed to this end perform either face recognition or facial expression …

In this paper, we propose an automatic approach for localizing the inner eye canthus in
thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center …