Download - Face recog - slideshare
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Face Recognition System
Pratik Tyagi
9911103604
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Face Recognition from video.
– How to learn a facial model from the
data coming from the face detector?
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Face Recognition from video.
• Challenges:1) How to learn INVARIANTLY to spatial transformations?
Simultaneous registration and Subspace computation.
2) How to select the most discriminative features?
3) How to deal with missing data?
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Face Recognition from video.
–Register w.r.t a Subspace
–Selecting the most discriminative samples.
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Face Recognition from video.
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Distance between Sets A and B.
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- How to exploit temporal redundancy in the recognition process?
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Face Recognition from video.
• 95 % of recognition rate (11 Subjects and 30 images per subject).
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Plans year 2.
• Why is hard to perform face recognition from
Mosaic images?– Small images.
– Noisy images.
– Misalignments.
• But …– Temporal redundancy.
– Recognizing several people (exclusive principle).
– Superesolution.
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Learning person-specific models.
• Unsupervised learning from video sequences:– Facial appearance models.– Behaviour models (e.g. gestures).
• Learning person-specific models can be useful to identify people, to predict actions?
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Meeting visualization/summarization
• Input: – Set of several videos, with detected and
recognized faces. – Set of indicators if the person is talking, up,
down, etc…
• Output:– Low dimensional visualization of the meeting
activity and interaction between people.– Learning interaction models between people.