title: Total variation models for variable lighting face recognition.
abstract: In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions.
authors.affiliation: University of Illinois at Urbana Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA. tchen5@ifp.uiuc.edu
authors.forename: Terrence
authors.initials: T
authors.lastname: Chen
authors.forename: Wotao
authors.initials: W
authors.lastname: Yin
authors.forename: Xiang Sean
authors.initials: XS
authors.lastname: Zhou
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001185:Artificial Intelligence; D003198:Computer Simulation; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008029:Lighting; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity