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Face recog - slideshare
- 1. Face Recognition System Pratik Tyagi 9911103604
- 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: 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? ∑= −= T t tt E 1 2 )()(),,( W1t cB)af(x,daCB
- 4. Face Recognition from video. –Register w.r.t a Subspace –Selecting the most discriminative samples.
- 5. Face Recognition from video. ∑∑ ∑∑ = i j BA i j j BT i ABA ji ji d λλ λλ uu BA, )( Distance between Sets A and B. Singular vectors of A A= B= - How to exploit temporal redundancy in the recognition process?
- 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.
- 10. 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.