Sparse eigentracker augmented by associative mapping to 3D shape

Yuki Oka, Takeshi Shakunaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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).

Original languageEnglish
Title of host publication2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
Pages649-656
Number of pages8
DOIs
Publication statusPublished - Jun 17 2011
Event2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011 - Santa Barbara, CA, United States
Duration: Mar 21 2011Mar 25 2011

Publication series

Name2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011

Other

Other2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
CountryUnited States
CitySanta Barbara, CA
Period3/21/113/25/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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  • Cite this

    Oka, Y., & Shakunaga, T. (2011). Sparse eigentracker augmented by associative mapping to 3D shape. In 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011 (pp. 649-656). [5771327] (2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011). https://doi.org/10.1109/FG.2011.5771327