Face annotation for personal photos using collaborative face recognition in online social networks

Authors:
Jae Young Choi;Wesley De Neve;Yong Man Ro;Konstantinos N. Plataniotis
Affiliations:
Image and Video Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon, Korea;Image and Video Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon, Korea;Image and Video Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon, Korea;Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

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Abstract

Automatic face annotation (or tagging) facilitates improved retrieval and organization of personal photos in online social networks. In this paper, we present a new collaborative face recognition (FR) method that aims to improve face annotation accuracy. The proposed method makes efficient use of multiple FR engines and databases that are distributed over an online social network. The performance of our collaborative face recognition method was successfully evaluated using the standard MPEG-7 VCE-3 data set and a set of real-world personal photos from the web. The efficacy of the proposed method is demonstrated in terms of comparative annotation performance against noncollaborative approaches utilizing a single FR engine and a single database only.