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SIMULTANEOUS MULTI-VIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERING

dc.contributor.advisorChellappa, Ramaen_US
dc.contributor.authorSeo, Naotoshien_US
dc.date.accessioned2009-07-02T06:12:04Z
dc.date.available2009-07-02T06:12:04Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1903/9286
dc.description.abstractRecently, face recognition based on video has gained wide interest especially due to its role in surveillance systems. Video-based recognition has superior advantages over image-based recognition because a video contains image sequences as well as temporal information. However, surveillance videos are generally of low-resolution and contain faces mostly in non-frontal poses. We propose a multi-view, video-based face recognition algorithm using the Bayesian inference framework. This method represents an appearance of each subject by a complex nonlinear appearance manifold expressed as a collection of simpler pose manifolds and the connections, represented by transition probabilities, among them. A Bayesian inference formulation is introduced to utilize the temporal information in the video via the transition probabilities among pose manifolds. The Bayesian inference formulation realizes video-based face recognition by progressively accumulating the recognition confidences in frames. The accumulation step possibly enables to solve face recognition problems in low-resolution videos, and the progressive characteristic is especially useful for a real-time processing. Furthermore, this face recognition framework has another characteristic that does not require processing all frames in a video if enough recognition confidence is accumulated in an intermediate frame. This characteristic gives an advantage over batch methods in terms of a computational efficiency. Furthermore, we propose a simultaneous multi-view face tracking and recognition algorithm. Conventionally, face recognition in a video is performed in tracking-then-recognition scenario that extracts the best facial image patch in the tracking and then recognizes the identity of the facial image. Simultaneous face tracking and recognition works in a different fashion, by handling both tracking and recognition simultaneously. Particle filter is a technique for implementing a Bayesian inference filter by Monte Carlo simulation, which has gained prevalence in the visual tracking literature since the Condensation algorithm was introduced. Since we have proposed a video-based face recognition algorithm based on the Bayesian inference framework, it is easy to integrate the particle filter tracker and our proposed recognition method into one, using the particle filter for both tracking and recognition simultaneously. This simultaneous framework utilizes the temporal information in a video for not only tracking but also recognition by modeling the dynamics of facial poses. Although the time series formulation remains more general, only the facial pose dynamics is utilized for recognition in this thesis.en_US
dc.format.extent1136581 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleSIMULTANEOUS MULTI-VIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERINGen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledEngineering, Electronics and Electricalen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledComputer Visionen_US
dc.subject.pquncontrolledFace Recognitionen_US
dc.subject.pquncontrolledFace Trackingen_US
dc.subject.pquncontrolledImage Processingen_US
dc.subject.pquncontrolledPartcile Filteringen_US


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