Gölge, ErenDuygulu, Pınar2016-02-082016-02-082015-06http://hdl.handle.net/11693/28185Date of Conference: 7-12 June 2015Conference name: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)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.EnglishAccuracyBuildingsComputational modelingData modelsFaceNoise measurementTrainingBuildingsClassification (of information)Data structuresFace recognitionIterative methodsPattern recognitionPersonnel trainingAccuracyBenchmark datasetsComputational modelFaceFace identificationModel evolutionNoise measurementsState of the artComputer visionConference Paper10.1109/CVPRW.2015.7301353