RECOGNIZING FACIAL EXPRESSIONS

THROUGH TRACKING

Salih Burak Gokturk

OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

MOTIVATIONWhat does expression recognition mean?

Guessing the meaning of facial deformations.

What are the possible applications ?- Any interactive scenerio.- Video conferencing

Why do we want to use 3-D information ?

- 2-D system is very dependent on the view yet computationally simple.

Components of the recognition system

Analysis -Face Tracking

Intelligence-Support Vector Machine

Classifier

Shape Parameters

OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

SNAKE MODEL• Introduced by Kass and Witkin.• Energy Minimization Problem:

• Used by Waters and Terzopoulos for tracking.• Snakes are fit to important regions, and tracked

from one view to the other.

MODEL BASED 3-D FACE TRACKING

• DeCarlo and Metaxas, ’96, very accurate tracking.• Eisert and Girod, ’98, used in video-conferencing

application, with efficient and accurate compression.• Gokturk et. al., ’00, brings a data driven approach

where the face model is learnt from stereo tracking. • Model based approaches.• Track all the points together with n-dimensional

freedom on the shape. • Based on Lukas-Tomasi-Kanade optical flow tracker.

OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

PROBLEM DESCRIPTION(Tracking 1)

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PROBLEM DESCRIPTION(Tracking 2)

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I(x(t)) I(t+1)

TIME t+1

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PROBLEM DESCRIPTION (Recognition)

X(t)[ Rigid, Open Mouth, Smile]

?[ Rigid, Open Mouth, Smile]

TrainingData Classifier TestingNew Data Output

OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

ASSUMPTIONS

• Cameras are calibrated. • The person should move slow unless the camera is fast enough for motion capture.• The mesh is initialized to the first image.• The user performs the expressions known to the computer

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p - degrees of freedom

- shape is learnt from stereo learning in our case

Stereo TrackingData Monocular TrackingLearn Shape

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Il(xi(t+1))Time t+1:

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uII

- For robustness, u and v are estimated using a neighbourhood around the point.

LUKAS TOMASI KANADE OPTICAL FLOW TRACKER

LUKAS TOMASI KANADE OPTICAL FLOW TRACKER EXTENDED TO 3D

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OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

STEREO LEARNING

FACE TRACKING

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- The deformation space of a particular individual is learnt- That particular individual is tracked using a mono camera

- Tracked parameters : , R, T.

UNIVERSAL FACE TRACKER-The deformation space of a subset of people learnt- The shapes are aligned and PCA is applied on this set

OVERVIEW• MOTIVATION• PREVIOUS WORK• PROBLEM DESCRIPTION• THEORY OF TRACKING• TRACKING VIDEOS• THEORY OF SVM

Support Vector Machines (SVM)

- Best discriminating hyperplane between two class of objects

- Distinguish the vectors that carry the relevant information (support vectors)

- if nonlinear data, map the data to high dimensional domain, then apply the SVM.

TrainingData Classifier TestingNew Data Output

Expected Contributions

- Show that 3-D model based tracking is suitable for further applications. - Support vector machine is a suitable classifier for expression recognition

What needs to be done

- Choose the appropriate data input for SVM classifier. Using vector might not help. Create more intelligent vectors in that case.- Choose an appropriate kernel (transformation function) for SVM. - Apply SVM in a one to many fashion.- Combination of intelligent features and SVM should give robust results.