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Research Update and Future Work Directions
– Jan 18, 2006 –
Ognjen ArandjelovićRoberto Cipolla
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Overview
Research update:
1. Face recognition from video for
i. User authentication
ii. Multimediaretrieval/organization
2. Acquisition conditions-adaptiveimage filtering
3. Local manifold illumination-invariants
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AFR from Video: Authentication (ECCV)
Key ideas:
• Sequence re-illumination algorithm
• Offline learning: generic effects of illumination across human face shape variation
Addressed invariance to:
1. Illumination
2. Pose
3. User motion pattern
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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)
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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
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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
My clusters
Single cluster
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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
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A New Look at Filters for AFR (FG)A summary of the results:
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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
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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:
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Colour invariants for AFRKey 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
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AFR for Content-Based Retrieval and Synthesis
Combine:
• Face recognition
• Texture/Segmentation
• Local features-basedretrieval
• Image mosaicing
Retrieval query interface tool