Emotion Recognition Using Facial Expressions

EMOTION RECOGNITION USING FACIAL EXPRESSIONS

Guided by, Remya Krishna J.S Asst.Professor 1

Dept. of ECE

Presented by, Jismy Jelson LTJE16EC028 S7 ECE

CONTENTS Introduction  Emotions  Working  Classifiers  Advantages  Disadvantages  Applications  Future Scope  Conclusion  References 

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INTRODUCTION Emotion recognition is the process of identifying human emotion, most typically from facial expressions as well as from verbal expressions  extracting and understanding of emotion has a high importance of the interaction between human and machine communication. 

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WHAT IS EMOTION

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HISTORY 

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Tomkins 1964 Started In 1978 Paul Ekman introduced a system Facial Action Coding System(FACT). Based on Paul Ekman's 6 Basic Emotions.

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USED ITEMS 

CAMERA

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MICROSOFT KINECT 3D SOFTWARE

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CLASSIFIER

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WORKING

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FEATURE EXTRACTION

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Appearance Features

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Geometric Features

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GEOMETRIC FEATURE EXTRACTION 

A three-state lip model describes the lip state: open, closed, tightly closed. A two-state model (open or closed) is used for each of the eyes. Each brow and cheek has a one-state model.

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APPEARANCE FEATURE EXTRACTION Gabor wavelets are widely used to extract the facial appearance changes as a set of multi scale and multi orientation coefficients. The Gabor filter may be applied to specific locations on a face or to the whole face image.

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ACTION UNITS

AUs are considered to be the smallest visually discernible facial movements.  As AUs are independent of any interpretation, they can be used as the basis for recognition of basic emotions.  However, both timing and the duration of various AUs are important for the interpretation of human facial behavior.  It is an unambiguous means of describing all possible movements of face in 46 action points . 

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appearance features. Geometric features

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CLASSIFIERS K-NN CLASSIFIER

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K Nearest Neighbors Simple algorithm Nonlinear classifier 96% accuracy

MLP CLASSIFIER

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Multilayer Perception Include 3 layers Class of artificial neural network 90% accuracy

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KNN CLASSIFIER

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KNN classifier cont.…..

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KNN classifier cont.….. ADVANTAGES

DISADVANTAGES

Very simple  Easy to understand  Effective  Sensitive to noise

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Choosing k is tricky  Expensive  No training stage

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ADVANTAGES 

Lower complexity

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Less computer demanding

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Sensitive

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Real time monitoring

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More secured 20

DISADVANTAGES Pose and Frequent head movements  Presence of structural components  Occlusion  Image orientation  Imaging conditions  Subtle facial deformation  Ambiguity and uncertainty in face motion measurement 

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APPLICATIONS Predictive environments (Ambient Intelligence).  More human-like human-computer, and human robot  interaction (e.g: emotional avatar).  Emotional Mirror (Affective Computing).  Treatment for people with psycho-affective  illnesses (e.g: autism).  Distance learning  Robotics 

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Emotion recognition in Health Care  Automotive industry and emotion recognition  Emotion recognition in video game testing  Educational 

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SELF DRIVING

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FUTURE SCOPE Handle partial occlusions better.  Make it more robust (lighting conditions etc.)  More person independent (fit mask automatically).  Use other classifiers (dynamics).  Apply emotion recognition in applications. For example games 

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CONCLUSION 

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Facial emotion recognition has more challengs.by using K-NN classifier we can reduce some of this. Developing technology’s using. Movements of head will effect the emotion recognition.

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REFERENCES International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland.  P. Ekman. Emotions Revealed: Recognizing Faces and Feeling to Improve Communication and Emotional Life. Holt, 2003.  Ratliff M. S., Patterson E., Emotion recognition using facial expressions with active appearance models, Proceedings of the Third IASTED International Conference on Human Computer Interaction, ACTA Press, Anaheim, CA, USA, 2008, 138–143.  Etc………….. 

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