Face Recognition across Large Pose Variations Via Boosted Tied Factor Analysis

TitleFace Recognition across Large Pose Variations Via Boosted Tied Factor Analysis
Publication TypeConference Paper
Year of Publication2011
AuthorsKhaleghian, S., H. R. Rabiee, and M. H. Rohban
Conference NameIEEE Workshop on Applications of Computer Vision (WACV) 2011
Date Published01/2011
Conference LocationKona, Hawaii
ISBN Number978-1-4244-9497-2
AbstractIn this paper, we propose an ensemble-based approach to boost performance of Tied Factor Analysis(TFA) to overcome some of the challenges in face recognition across large pose variations. We use Adaboost.m1 to boost TFA which has shown to possess state-of-the-art face recognition performance under large pose variations. To this end, we have employed boosting as a discriminative training in the TFA as a generative model. In this model, TFA is used as a base classi ̄ for the boosting algorithm and a weighteder likelihood model for TFA is proposed to adjust the importance of each training data. Moreover, a modi ̄ weighteding and a diversity criterion are used to generate more diverse classi ̄ in the boosting process. Experimental reers sults on the FERET data set demonstrated the improved performance of the Boosted Tied Factor Analysis(BTFA) in comparison with TFA for lower dimensions when a holistic approach is being used.
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