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CAMEO: Face Recognition Year 1 Progress and Year 2 Goals

CAMEO: Face Recognition Year 1 Progress and Year 2 Goals. Fernando de la Torre, Carlos Vallespi, Takeo Kanade. Face Recognition from video. How to learn a facial model from the data coming from the face detector?. Face Recognition from video. Challenges :

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CAMEO: Face Recognition Year 1 Progress and Year 2 Goals

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  1. CAMEO: Face RecognitionYear 1 Progress and Year 2 Goals Fernando de la Torre, Carlos Vallespi, Takeo Kanade

  2. Face Recognition from video. • How to learn a facial model from the • data coming from the face detector?

  3. Face Recognition from video. • Challenges: • 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?

  4. Face Recognition from video. • Register w.r.t a Subspace • Selecting the most discriminative samples.

  5. Face Recognition from video. • - How to exploit temporal redundancy in the recognition process? B= A= Distance between Sets A and B. Singular vectors of A

  6. Face Recognition from video. • 95 % of recognition rate (11 Subjects and 30 images per subject).

  7. 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.

  8. 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?

  9. 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.

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