att_abstract={{While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency, etc. Conventional wisdom is that by using sufficient data and machine, one can learn the systematic correlations and use the data to can form a more robust basis for core recognition tasks like verification, identification, and clustering. This however, takes large amounts of training data which is not really available for personal consumer photo collections. We address the fusion/correlation issue differently by proposing an ensemble-based approach that is built on different information sources such as facial appearance, visual context, and social (or co-appearance) information of samples in a dataset, to provide error diversity and higher classification accuracy for face recognition in consumer photo collections. To evaluate the utility of our ensembles and simultaneously generate stronger generic features, we perform two experiments - (i) a verification experiment on the standard unconstrained LFW (Labeled Faces in the Wild) dataset where by using an ensemble of appearance related features we report state-of-the art results on protocol-1 that provides 2.9% better classification accuracy than the current best method, and (ii) an identification experiment on the Gallagher personal photo collection where we demonstrate at least 17% relative performance gain using visual and social ensembles.}},
	att_authors={ez2685, rg4675},
	att_categories={C_IIS.13, C_CCF.10, C_IIS.10},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012  {{, 2013-09-29}}
	att_projects={Miracle, ATTCAE},
	att_tags={face recognition, consumer photos, visual attributes, face verification},
	author={Eric Zavesky and Raghuraman Gopalan and Archana Sapkota},
	institution={{IEEE International Conference on Biometrics: Theory, Applications and Systems}},
	title={{Appearance, Visual and Social Ensembles for Face Recognition in Personal Photo Collections}},