Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

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 …

The large pose discrepancy between two face images is one of the key challenges in face
recognition. Conventional approaches for pose-invariant face recognition either perform …

Photorealistic frontal view synthesis from a single face image has a wide range of
applications in the field of face recognition. Although data-driven deep learning methods …

Artificial neural networks are computational approaches based on machine learning to learn
and make predictions based on data, and have been applied successfully in diverse …

Anomalous event detection is of utmost importance in intelligent video surveillance.
Currently, most approaches for the automatic analysis of complex video scenes typically rely …

Pose and expression normalization is a crucial step to recover the canonical view of faces
under arbitrary conditions, so as to improve the face recognition performance. An ideal …

This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-
task convolutional neural network (CNN) for face recognition, where identity classification is …

Pose variation is one key challenge in face recognition. As opposed to current techniques
for pose invariant face recognition, which either directly extract pose invariant features for …