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Research Update and Future Work Directions – Jan 18, 2006 –

Research Update and Future Work Directions – Jan 18, 2006 –. Ognjen Arandjelovi ć Roberto Cipolla. Overview. Research update: Face recognition from video for User authentication Multimedia retrieval/organization Acquisition conditions-adaptive image filtering

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Research Update and Future Work Directions – Jan 18, 2006 –

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  1. Research Update and Future Work Directions– Jan 18, 2006 – Ognjen ArandjelovićRoberto Cipolla

  2. Overview • Research update: • Face recognition from video for • User authentication • Multimediaretrieval/organization • Acquisition conditions-adaptiveimage filtering • Local manifold illumination-invariants

  3. AFR from Video: Authentication (ECCV) • Addressed invariance to: • Illumination • Pose • User motion pattern • Key ideas: • Sequence re-illumination algorithm • Offline learning: generic effects of illumination across human face shape variation

  4. AFR from Video: Authentication (ECCV) • Key results: • Average recognition 99.7% on 171 people (over 1300 sequences) • Excellent generalization, even across race • Interesting findings on image filters for AFR • Future work: • Efficiency improvement (more compact representations of FMMs…) • Smarter use of image filters (different research direction)

  5. Automatic Cast Listing in Films (CVPR) Visually defined clustering – on face appearance manifolds • Key ideas: • Similarities between people exhibit coherence – exploited by working in the Manifold Space (each point a manifold) • Iterative unsupervised learning, bootstrapped using offline training

  6. Automatic Cast Listing in Films (CVPR) • Key results: • Algorithm needs more testing – only preliminary results in • Very promising improvement over simple clustering (inter-manifold distance thresholding) “Simple clustering” results Single cluster My clusters

  7. A New Look at Filters for AFR (FG) • Key ideas and methods: • Recognition performances of raw and filtered data negatively correlate (ECCV results) • Learn how to optimally combine raw input and filtered data • Implicit learning of the severity of data acquisition conditions • We propose a heuristic, iterative algorithm

  8. A New Look at Filters for AFR (FG) A summary of the results:

  9. Local Manifold Illumination Invariant (ICPR) • Method overview: • Consider the generative function of the face appearance manifold • We show that the angles between hyperplanes of small head motion are invariant under illumination changes • Manifold is represented as a redundant set of locally linear patches

  10. Probabilistic Extension of MSM (ICPR) • MSM limitations: • Information loss with subspace dimensionality choice • Within subspace, all directions treated the same – decreased SNR • Key idea: • Find the most probable “mutual mode” Efficiently computed similarity:

  11. Colour invariants for AFR • Key ideas: • Colour used extensively for detection applications – very little research on its use for recognition • Step 1: Model non-linear response of the photometric sensor • Step 2: Recover model parameters • Step 3: Camera/illumination invariants

  12. AFR for Content-Based Retrieval and Synthesis • Combine: • Face recognition • Texture/Segmentation • Local features-basedretrieval • Image mosaicing Retrieval query interface tool

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