1. University of Coimbra ISR – Institute of Systems and Robotics University of Coimbra - Portugal Institute of Systems and Robotics http://paloma.isr.uc.pt

2. University of Coimbra Determining face orientation for a robot able to interpret facial expressions Carlos Simplício, José Prado and Jorge Dias Presented by José Prado 2010 03 - 10 Human-Robot Interaction

3. University of Coimbra Summary Human-Robot Interaction I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

4. University of Coimbra Summary Human-Robot Interaction I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

5. University of Coimbra Introduction We are developing a service/assistant robot, an Autonomous Mobile Agent (AMA). This agent, will be used in the context of assisted ambiance. The global project addresses the emergent tendencies to develop new devices for assistance and services.

6. University of Coimbra Introduction Human beings express their emotional states through: facial expressions gestures voice etc. We propose: a technique to determine face orientation based in human face symmetry; a DBN to classify human facial expressions.

7. University of Coimbra Introduction The AMA must observe and react according facial expressions of a person. Facial expressions recognition becomes easier if done in frontal face images. The robotic system will be used to follow the human being movements and keeps always a frontal face.

8. University of Coimbra Summary I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

9. University of Coimbra AMA - architecture

10. University of Coimbra Summary I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

11. University of Coimbra Robotic System Controller - RSC Robotic Platform movements: – Longitudinal translations; – Transversal translations; – Rotations. Rotations correspond to an arc of circle centered in the human being. Objective is to follow the rotation done by the human being, getting always an image of a frontal face. Robotic Head can move in synchronization. 1 2

12. University of Coimbra Summary I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

13. University of Coimbra Face Pose Identification System - FPIS In a perfect symmetric image, pixels positioned symmetrically have the same gray-level value: difference is zero. We use this principle to verify if an image is symmetric: frontal face. Example 1 Example 2

14. University of Coimbra Face Pose Identification System - FPIS In a perfect symmetric image, pixels positioned symmetrically have the same gray-level value: difference is zero. Problems: By nature, human faces are not perfectly symmetric; There are shadows. But it works!!!

15. University of Coimbra Face Pose Identification System - FPIS Define a vertical axis (always in the same position); Calculate differences of gray-levels between symmetric (position) pixels. Build Normalized Gray-level Difference Histogram (NGDH). In a frontal face, the vertical axis bisects the face and the information collected in the NGDH is strongly concentrated near the mean. Else, the information is scattered along the NGDH. NGDH with scattered information NGDH with concentrated information

16. University of Coimbra Face Pose Identification System - FPIS Algorithm: Find and extract face region in the image; Define a vertical axis (dividing the region in two parts with equal number of pixels); Synthesize face images - use vertical axis to perform a 3D transformation (rotation); Synthesized images are “hypotheses” to find the face out-of-plane rotation; Built NGDH's; Find the pseudomean – number of occurrences in a narrow region around the NGDH's mean; Synthesized image with great pseudomean has the frontal face!!

17. University of Coimbra Face Pose Identification System - FPIS

18. University of Coimbra Face Pose Identification System - FPIS Algorithm: Find and extract face region in the image; Define a vertical axis (dividing the region in two parts with equal number of pixels); Synthesize face images - use vertical axis to perform a 3D transformation (rotation); Synthesized images are “hypotheses” to find the face out-of-plane rotation; Built NGDH's; Find the pseudomean – number of occurrences in a narrow region around the NGDH's mean; Synthesized image with great pseudomean has the frontal face!!

19. University of Coimbra Face Pose Identification System - FPIS

20. University of Coimbra Face Pose Identification System - FPIS Algorithm: Find and extract face region in the image; Define a vertical axis (dividing the region in two parts with equal number of pixels); Synthesize face images - use vertical axis to perform a 3D transformation (rotation); Synthesized images are “hypotheses” to find the face out-of-plane rotation; Built NGDH's; Find the pseudomean – number of occurrences in a narrow region around the NGDH's mean; Synthesized image with great pseudomean has the frontal face!!

21. University of Coimbra Face Pose Identification System - FPIS

22. University of Coimbra Face Pose Identification System - FPIS

23. University of Coimbra Face Pose Identification System - FPIS Algorithm: Find and extract face region in the image; Define a vertical axis (dividing the region in two parts with equal number of pixels); Synthesize face images - use vertical axis to perform a 3D transformation (rotation); Synthesized images are “hypotheses” to find the face out-of-plane rotation; Built NGDH's; Find the pseudomean – number of occurrences in a narrow region around the NGDH's mean; Synthesized image with great pseudomean has the frontal face!!

24. University of Coimbra Face Pose Identification System - FPIS

25. University of Coimbra Face Pose Identification System - FPIS Original Angle = 0º Rotation 0º Rotation +30º Result -30º Result 0º Result +30º Rotation -30º

26. University of Coimbra Face Pose Identification System - FPIS Original Angle = -30º Rotation -30º Rotation 0º Rotation +30º Result -60º Result -30º Result 0º

27. University of Coimbra Face Pose Identification System - FPIS Original Angle = +30º Rotation -30º Rotation 0º Rotation +30º Result 0º Result +30º Result +60º

28. University of Coimbra Summary I ntroduction (Interactive Mobile Robots)‏ Autonomous Mobile Agent (AMA)‏ Robotic System Controller (RSC)‏ Face Pose Identification System (FPIS)‏ Automatic Facial Expressions Recognition System (AFERS)‏ ( Structure of a DBN classifying facial expressions)‏

29. University of Coimbra Facial Expressions We only consider five emotional states. Each emotional state has a characteristic facial expression. A facial expression is a set of Action Units (AUs). PauloJoséCarlos Alex angerfearhappyneutralsad AUs are “distortions” of facial features. Ex: lips smile.

30. University of Coimbra DBN's Structure

31. University of Coimbra DBN's Structure Level 1 Emotional States considered are:  anger  fear  happy  sad  neutral  other Node (variable) that probabilistically reflect the existence of an Emotional State.

32. University of Coimbra DBN's Structure Level 2 Expressions considered are:  anger  fear  happy  sad  neutral Nodes (variables) that probabilistically reflect the existence of a facial expression....

33. University of Coimbra DBN's Structure Level 3 For the happy facial expression:  EH-AU1N  EH-AU25P Negative evidence Positive evidence 11 AUs are considered in each facial expression. Must be absent Must be present...

34. University of Coimbra DBN's Structure Level 3 11 AUs are considered in each facial expression.

35. University of Coimbra DBN's Structure Level 4 Nodes (variables) that probabilistically reflect the strength of the evidences (positive or negative)....

36. University of Coimbra DBN's Structure Level 5 Here, information is propagated between time slices. These nodes (variables) combine / fuse probabilistically, through inertia, information coming from the low level in present time slice with that from the previous instant....

37. University of Coimbra DBN's Structure Level 6 Nodes (variables) collecting the evidences provided by the sensors....

38. University of Coimbra Conclusions It was developed: An architecture for an Autonomous Mobile Agent; A Face Orientation Identification Technique; A structure for a DBN. The Face Pose Identification Technique has a good performance and is very fast. Classification of facial expressions using positive and negative evidences is very promising.

39. University of Coimbra END Thanks for your attention!!! Questions ?