Paper Info
Title
From Rank-N to Rank-1 Face Recognition Based on Motion Similarity
Abstract
In this paper, we present a sequential framework using facial motion information as a subsidiary to improve face recognition performance. As is generally known, reasonable static face recognition has been achieved based on subspace reduction techniques. In order to further improve performance, some extra cues, such as temporal variation, are investigated by building dynamic models. We propose a permuted similarity motion fea- ture and integrate it into a sequential recognition system. This system can select the best candidate from the Rank-N candidates picked up in the recognition step based on static appearance parameters by using motion information. The recognition rate of the motion similarity is compared with the motion feature obtained from auto-regressive models to prove its efficiency. In addition, the sequential system achieves better performance when the motion information is integrated with the static appearance information in a flexible manner.
Year
Venue
Keywords
2009
BMVC
auto regressive,face recognition
Field
DocType
Citations 
Computer vision,Facial recognition system,3D single-object recognition,Subspace topology,Pattern recognition,Recognition system,Computer science,Artificial intelligence,Dynamic models,Motion estimation
Conference
3
PageRank 
References 
Authors
0.52
19
2
Name
Order
Citations
PageRank
Hui Fang110018.10
Nicholas Costen222828.42