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Carnegie Mellon Robotics Institute

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Abstract

Meetings are an integral part of business life. In previous work, we have developed a physical awareness system called CAMEO (Camera Assisted Meeting Event Observer) to record and process audio/visual information of a meeting. A very important task in meeting understanding is to know who is attending to the meeting and CAMEO's task is to infer people's identity from video. In this paper, we present an approach to identify people from an omnidirectional video sequence. Two main novelties are proposed: first a new dimensionality reduction technique MODA (Multimodal Oriented Discriminant Analysis) is used to perform fast matching and second we show that using multiple spatio-temporal constraints the recognition performance greatly improves. The effectiveness and robustness of the proposed system is demonstrated over several real time experiments and a large data set of videos.

Keywords

Face Recognition, Subspace Methods, Dimensionality Reduction.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): People Image Analysis Consortium, MultiRobot Lab, Face Group, Component Analysis

Associated Project(s): Camera Assisted Meeting Event Observer and Face Recognition

Number of pages: 8

Text Reference

Fernando De la Torre Frade, Carlos Vallespi-Gonzalez, Paul Rybski, Manuela Veloso, and Takeo Kanade, "Multiple Face Recognition from Omnidirectional Video," IEEE Workshop on Learning in Computer Vision and Pattern Recognition, June, 2005.

BibTeX Reference

@inproceedings{De_la_Torre_Frade_2005_5019,
   author = "Fernando {De la Torre Frade} and Carlos Vallespi-Gonzalez and Paul Rybski and Manuela Veloso and Takeo Kanade",
   title = "Multiple Face Recognition from Omnidirectional Video",
   booktitle = "IEEE Workshop on Learning in Computer Vision and Pattern Recognition",
   month = "June",
   year = "2005",
}

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