Face Recognition System

Pratik Tyagi

9911103604

Face Recognition from video.

– How to learn a facial model from the

data coming from the face detector?

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.

Face Recognition from video.

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- How to exploit temporal redundancy in the recognition process?

Face Recognition from video.

• 95 % of recognition rate (11 Subjects and 30 images per subject).

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.

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?

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.