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 …

Face images captured in unconstrained environments usually contain significant pose
variation, which dramatically degrades the performance of algorithms designed to recognize …

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 …

Mosaicing entails the consolidation of information represented by multiple images through
the application of a registration and blending procedure. We describe a face mosaicing …

Pose-invariant face recognition (PIFR) refers to the ability that recognizes face images with
arbitrary pose variations. Among existing PIFR algorithms, pose normalization has been …

One of the key challenges for current face recognition techniques is how to handle pose
variations between the probe and gallery face images. In this paper, we present a method …

We propose a novel pose-invariant face recognition approach which we call Discriminant
Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for …

An ideal approach to the problem of pose-invariant face recognition would handle
continuous pose variations, would not be database specific, and would achieve high …

Face recognition algorithms perform very unreliably when the pose of the probe face is
different from the gallery face: typical feature vectors vary more with pose than with identity …

The paper addresses the problem of pose-invariant recognition of faces via an MRF
matching model. Unlike previous costly matching approaches, the proposed algorithm …