The capacity to recognize faces under varied poses is a fundamental human ability that
presents a unique challenge for computer vision systems. Compared to frontal face …

One of the major challenges encountered by current face recognition techniques lies in the
difficulties of handling varying poses, ie, recognition of faces in arbitrary in-depth rotations …

We train a generative convolutional neural network which is able to generate images of
objects given object type, viewpoint, and color. We train the network in a supervised manner …

Face recognition under viewpoint and illumination changes is a difficult problem, so many
researchers have tried to solve this problem by producing the pose-and illumination …

Automated program repair is the problem of automatically fixing bugs in programs in order to
significantly reduce the debugging costs and improve the software quality. To address this …

Most existing pose robust methods are too computational complex to meet practical
applications and their performance under unconstrained environments are rarely evaluated …

Modern information technology paradigms, such as online services and off-the-shelf
products, often involve a wide variety of users with different or even conflicting objectives …

3D face reconstruction algorithms from images and videos are applied to many fields, from
plastic surgery to the entertainment sector, thanks to their advantageous features. However …

Face recognition has made significant advances in the last decade, but robust commercial
applications are still lacking. Current authentication/identification applications are limited to …

We address the problem of 3D-assisted 2D face recognition in scenarios when the input
image is subject to degradations or exhibits intra-personal variations not captured by the 3D …