%0 Conference Proceedings
%T Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition
%A Fadi Dornaika
%A Alireza Bosaghzadeh
%A Bogdan Raducanu
%B Human Behavior Understanding 4th International Workshop
%D 2013
%V 8212
%I Springer International Publishing
%@ 0302-9743
%@ 978-3-319-02713-5
%F Fadi Dornaika2013
%O OR;MV
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2315), last updated on Tue, 18 Oct 2016 16:12:51 +0200
%X Workshop on Human Behavior UnderstandingHuman-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods.
%U http://refbase.cvc.uab.es/files/DBR2013.pdf
%U http://dx.doi.org/10.1007/978-3-319-02714-2_11
%P 124-135