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Tutorial Topic Facial Expression & Emotion Detection for Man-Machine Interaction

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Page 1: Face Detection ppt

Tutorial TopicFacial Expression & Emotion Detection for

Man-Machine Interaction

Page 2: Face Detection ppt

Two legends of their own..People we can’t forget..

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Technology has made them eternal..

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Why Emerging Trend???

• The demand for humanoid robots as service robots for everyday life has increased during the last years.

• Detection of emotions which enables the robot to react appropriate to the emotional state of the communication partner.

• Humanoid robots are - as it can be seen in many movies - of great interest. Either as entertainment robots, enduring workers or for the care of elderly people.

• All these possible scenarios have one thing in common: the robot is an accepted member of society and therefore it must behave as a human would do.

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Outline-

Facial Expression Estimation Face Detection Facial Feature Extraction Anatomical Constraints – Anthropometry FP Localization FAP Calculation Expression Profiles

Gesture Analysis

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Face Detection

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Facial Feature Extraction

• Multiple cue Facial Feature Boundary Extraction :– Eyebrows

– Eyes

– Nose

– Mouth

• Each mask is either Edge-based mask or Intensity-based mask.

• Each mask is validated independently.

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Multiple Cue Facial Feature Extraction an example-

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ANTHROPOMETRY - final mask validation

Facial distances Male/Female

separation measured by the US Army

(30 year period )

The measured distances are normalized by division with Distance 7, i.e. the distance between the inner corners of left and right eye, both points the human cannot move.

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DA5n, DA10n: distances in figures normalized by division with distance DA7:

(DA5n=DA5/DA7, DA10n=DA10/DA7)

DAewn: eye width (calculated from DA5 and DA7)

DAewn=((DA5-DA7)/2)/DA7

D5n DA5n_m

in DA5n_m

ax D10n

DA10n_min

DA10n_max

Dew_ln Dew_rn DAewn_

min DAewn_max

2.129 2.517 3.349 0.919 1.031 1.515 0.677 0.452 0.840 1.077

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FAP (Facial Animation Parameters)

Discrete features offer a neat, symbolic representation of

expressions

Not constrained to a specific face model Suitable for face cloning applications

MPEG-4 compatible: unified treatment of analysis and

synthesis parts In MMI environments.

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FAPs estimation

AU Description

1 Inner brow raiser

2 Outer brow raiser

4 Brow lowerer

10 Upper lip raiser

12 Lip corner puller

15 Lip corner depressor

20 Lip stretcher

24 Lip presser

26 Jaw drop

Detectable action units with feature points

1 1 22

4 4

10

1212 1515

26

2020

24 24

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Expression ProfilesEmotion Orignal

DefinitionAdapted Definition

Fear 1+2+4+5+20+25 (1L+1R+2L+2R+20L+20R)/6

Surprise 1+2+5+26 (1L+1R+2L+2R+26+26)/6

Anger 4+5+7+24 (4L+4R+24L+24R)/4

Sadness 1+4+15 (4L+4R+15L+15R)/4

Disgust 4+9+10+17

(4L+4++10)/3

Happiness 6+12+25 (12L+ 12R)/2

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Gesture Analysis Gestures too ambiguous to indicate

emotion on their own Gestures are used to support the

confidence outcome of facial expression analysis

Emotion Gesture Class

Joy hand clapping-high frequency

Sadness hands over the head-posture

Angerlift of the hand- high speed

italianate gestures

Fearhands over the head-gesture

italianate gestures

Disgustlift of the hand- low speed

hand clapping-low frequency

Surprise hands over the head-gesture

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Emotion analysis system overview

f : Values derived from the calculated distances

G : the value of a corresponding FAP

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System Interface

calculated FP distances

rules activated

recognized emotion

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Conclusion

• This system is divided into two main parts the feature detection and the emotion interpretation.

• Estimation of a user’s emotional state based on a fuzzy rules architecture.

• Evaluation approach based on anthropometric models and measurements.

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Thank You!!