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

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

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 …

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 …

Face recognition is the process of identifying people through facial images. It has become
vital for security and surveillance applications and required everywhere including …

This paper concerns the problem of facial landmark detection. We provide a unique new
analysis of the features produced at intermediate layers of a convolutional neural network …

Synthesizing realistic profile faces is promising for more efficiently training deep pose-
invariant models for large-scale unconstrained face recognition, by populating samples with …

Synthesizing realistic profile faces is beneficial for more efficiently training deep pose-
invariant models for large-scale unconstrained face recognition, by augmenting the number …

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 …

The objective of this paper is to learn a compact representation of image sets for template-
based face recognition. We make the following contributions: first, we propose a network …