FAME: Face association through model evolution
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
43 - 49
Item Usage Stats
MetadataShow full item record
We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the models associated to a name to evolve. The idea is based on capturing discriminative and representative properties of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with different characteristics. On benchmark datasets, our results are comparable to or better than the state-of-the-art studies for the task of face identification. © 2015 IEEE.
Classification (of information)
State of the art
Published Version (Please cite this version)http://dx.doi.org/10.1109/CVPRW.2015.7301353
Showing items related by title, author, creator and subject.
Okay, Kaan (Bilkent University, 2015)The prediction of corporate bankruptcies has been widely studied in the finance literature. This paper investigates business failures in non-financial Turkish companies between the years 2000 and 2015. I compare the ...
Tekinerdoǧan, Bedir; Aktekin, N. (ACM, 2009-10)One of the basic pillars in Model-Driven Software Development (MDSD) is defined by model transformations and likewise several useful approaches have been proposed in this context. In parallel, domain modeling plays an ...
Influence of phase function on modeled optical response of nanoparticle-labeled epithelial tissues Cihan, C.; Arifler, D. (2011)Metal nanoparticles can be functionalized with biomolecules to selectively localize in precancerous tissues and can act as optical contrast enhancers for reflectance-based diagnosis of epithelial precancer. We carry out ...