Paper Info
Title
Sparse eigentracker augmented by associative mapping to 3D shape
Abstract
This paper proposes real-time face tracking and recognition by combining two eigen-based methods. The first method is a novel extension of eigenface called augmented eigenface and the second method is a sparse 3D eigentemplate tracker controlled by a particle filter. The augmented eigenface is an eigenface augmented by an associative mapping to 3D shape that is specified by a set of volumetric face models. This paper discusses how to make up the augmented eigenface and how it can be used for inference of 3D shape from partial images. The associative mapping is also generalized to subspace-to-one mappings to cover photometric image changes for a fixed shape. A novel technique, called photometric adjustment, is introduced for simple implementation of associative mapping when an image subspace should be combined to a shape. The sparse 3D eigentemplate tracker is an extension of the 3D template tracker proposed by Oka et al. In combination with the augmented eigenface, the sparse 3D eigentemplate tracker facilitates real-time 3D tracking and recognition when a monocular image sequence is provided. In the tracking, sparse 3D eigentemplate is updated by the augmented eigenface while face pose is estimated by the sparse eigentracker. Since the augmented eigenface is constructed on the conventional eigenfaces, face identification and expression recognition are also accomplished efficiently during the tracking. Therefore, the task of face tracking and recognition is accomplished in the particle tracker framework. In the experiment, an augmented eigenface was constructed from 25 faces where 24 images were taken in different lighting conditions for each face. Experimental results show that the augmented eigenface works with the 3D eigentemplate tracker for real-time tracking and recognition. Real-time expression recognition was also reported where the augmented eigenface was constructed from several expressions of a particular person. The real-time tracker works at 30fps with quad-core implementation on Intel Core i7 CPU 940 (2.93GHz).
Year
DOI
Venue
2011
10.1109/FG.2011.5771327
Automatic Face & Gesture Recognition and Workshops
Keywords
Field
DocType
augmented reality,eigenvalues and eigenfunctions,emotion recognition,face recognition,image sequences,optical tracking,particle filtering (numerical methods),photometry,pose estimation,real-time systems,solid modelling,3D shape,Intel Core i7 CPU 940,associative mapping,augmented eigenface,eigen based method,expression recognition,face pose estimation,face recognition,monocular image sequence,particle filter,photometric adjustment,photometric image,quad-core implementation,real time face tracking,sparse 3D eigentemplate tracker,sparse Eigentracker
Facial recognition system,Computer vision,Eigenface,Subspace topology,Pattern recognition,Computer science,Particle filter,Augmented reality,Pose,Artificial intelligence,Pixel,Facial motion capture
Conference
ISBN
Citations 
PageRank 
978-1-4244-9140-7
3
0.46
References 
Authors
9
2
Name
Order
Citations
PageRank
Yuki Oka1101.36
Takeshi Shakunaga219243.46