PT Unknown
AU Fadi Dornaika
   Alireza Bosaghzadeh
   Bogdan Raducanu
TI Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition
BT Human Behavior Understanding 4th International Workshop
PY 2013
BP 124
EP 135
VL 8212
DI 10.1007/978-3-319-02714-2_11
AB 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.
ER