title: Multi-task pose-invariant face recognition.
abstract: Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.
authors.forename: Changxing
authors.initials: C
authors.lastname: Ding
authors.forename: Chang
authors.initials: C
authors.lastname: Xu
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011187:Posture / Q000502:physiology*