Paper Info
Title
Hierarchical approach to weight equations in face tracking and recognition framework
Abstract
In a real-time face tracking and recognition system proposed by Oka and Shakunaga, the weighted average of registered persons is calculated after photometric adjustment, and the weights are used for person identification and shape inference. Although that method works well for 25 persons, the computational cost is high when the number of registered persons increases. To solve this problem, this paper shows a hierarchical approach for efficient weight estimation. Although the hierarchical method only approximates the solution of the original equations, the approximation can suppress noise effects in person discrimination. In experiments, first the validity of the proposed method was checked on static data. Especially, a simple experiment on Multi-PIE data showed that both the original method and the proposed method can perfectly discriminate 249 faces. In tracking and recognition, we showed robust and fast person discrimination by introducing three quality levels into the discrimination rules. Combining the discrimination rules with the hierarchical approach, we remarkably improved the discrimination performance for 100-person face tracking and recognition. In another experiment, a simple variation of this scheme worked for 10-person identification when 10 expressions were registered for each person exhibiting many expressional changes, including pose and photometric changes.
Year
DOI
Venue
2013
10.1109/FG.2013.6553770
Automatic Face and Gesture Recognition
Keywords
Field
DocType
estimation theory,face recognition,image registration,object tracking,pose estimation,real-time systems,computational cost,discrimination performance,discrimination rules,face recognition framework,face recognition system,hierarchical method,multiPIE data,noise effects suppression,person discrimination,person face recognition,person face tracking,person identification,photometric adjustment,photometric change,pose change,real-time face tracking,registered persons,shape inference,static data,weight equations,weight estimation,weighted average
Computer vision,Facial recognition system,Pattern recognition,Expression (mathematics),Computer science,Inference,Pose,Video tracking,Artificial intelligence,Estimation theory,Facial motion capture,Image registration
Conference
ISSN
ISBN
Citations 
2326-5396
978-1-4673-5544-5
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Hisayoshi Chugan100.34
Takeshi Shakunaga219243.46