title: Misalignment-robust face recognition.
abstract: Subspace learning techniques for face recognition have been widely studied in the past three decades. In this paper, we study the problem of general subspace-based face recognition under the scenarios with spatial misalignments and/or image occlusions. For a given subspace derived from training data in a supervised, unsupervised, or semi-supervised manner, the embedding of a new datum and its underlying spatial misalignment parameters are simultaneously inferred by solving a constrained l1 norm optimization problem, which minimizes the l1 error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and/or image occlusions all validate the effectiveness of our proposed general formulation for misalignment-robust face recognition.
authors.affiliation: Department of Electrical and Computer Engineering, National University of Singapore, singapore. eleyans@nus.edu.sg
authors.forename: Shuicheng
authors.initials: S
authors.lastname: Yan
authors.forename: Huan
authors.initials: H
authors.lastname: Wang
authors.forename: Jianzhuang
authors.initials: J
authors.lastname: Liu
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Tang
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results