its too good

1. (Roll no- 91/MCA/090021)
Project under the guidance of
DR. AMLAN CHAKRABARTI
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2. What is Emotion?
 Emotions are reflected in voice, hand and body gestures,
and mainly through facial expressions.
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3. Facial Emotion
 There are six types of facial emotions.
Anger Fear Disgust Happy Sad Surprise
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4. Importance of emotion recognition
 Human beings express emotions in day to day
interactions.
 Understanding emotions and knowing how to react to
people’s expressions greatly enriches the interaction.
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5. Review Of Previous Work
 Image pre-processing
 Facial feature extraction
-select some point on feature region
-consider a matrix surrounding that point
-calculate distances
 Facial expression detection
Reff.: [“Optical flow based analyses to detect emotion from human facial image data”- Axel Besinger ,
Tamara Sztynda, Sara Lal , Carmen Duthoit, Johnson Agbinya, Budi Jap ,David Eager, Gamini
Dissanayake]
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6. Proposed Methodology
 Capture Image
 Image Pre-processing
-RGB to gray scale conversion
-Scale Normalization
 Crop Feature Regions
 Edge Detection
-Prewitt edge detection
 Facial Emotion Classification
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7. Methodology Co t…
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8. Capture Image
 Digital camera, web-cam
 Resolution must be fixed
 Distance between the web-cam and the face has to be
fixed.
 Position of face must be fixed
 Must not wear glasses or have facial hair.
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9. Capture I age Co t…
Normal Facial Image Emotional Facial Image
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10. RGB to gray scale conversion
Color Facial Image Grayscale Facial Image
 Reduce complexity.
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11. Scale Normalization
 Select only the face portion within a rectangular
frame
 Remove unnecessary information
 By defining region.
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12. Crop Feature Regions
 Region of Interest: two Eyes, two Eye brows, Lip
 By defining the particular regions.
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13. Edge detection
ROI ROI after edge detection
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14. Algorithm:
Input: A Sample Image
Output: Detected Edges
Step 1: Accept the input image
Step 2: Apply mask h1 and h3 (prewitt mask) to the input
image.
Step 3: Masks manipulation of h1 and h3 separately on
the input image
Edge Detection Co t…
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15. Edge detectio Co t…
Step 4: Results combined to find the absolute magnitude of the
gradient
Where
Step 5: The absolute magnitude is the output edges.
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16. Facial Emotion Classification
 Calculate displacement of the points.
 Calculate averages of those values.
 Calculate standard deviation (SD) of those averages.
 Compare this SD with a threshold.
 Get Emotion.
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17. Algorithm
 Step 1: Take a still image of a normal expression pic1
(say) of a human face.
 Step 2: Converts the color image to grayscale.
 Step 3: Crop the five facial image region of interest (ROI)
(eyes, eye brows and lip) from the image by defining
region.
 Step 4: Find edges of all image region.
 Step 5: Take a still image of a emotional face (angry or
happy) pic2 (say) of same person and repeat step 2, 3
and 4.
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18. Algorith Co t…
 Step 7: Comparing the deviation of edges of the
specified region of pic1 with pic2 by finding the
Euclidian distances of coordinate of each pixel.
 Step 8: Put the Euclidian distances in a array k (say).
 Step 9: Find the standard deviation (SD) from the
elements of array.
 Step 10: Comparing the SD with pre-define threshold
and get the emotions.
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19. Experiment & Result
 30 images were used to create the template.
 60 were tested.
Statistical Values of Three categories

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20. Experiment & Result
 Total 83.33% classify Facial Emotion.
63.33% were correct
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21. Experiment & Result
 80% happy Emotion classification were correct.
 50% Angry Emotion classification were correct.
 Almost 100% null classification were correct.
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22. Applications
 E-Learning system
 Robotics System
 Human-Computer interaction System
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23. Conclusion and Future Work
 Implementation through this process is quite easy.
 Have to improve over the capturing Process.
 ROI extracted manually
-That should be automatically.
 Edge detection procedure should be less complex.
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24. THANK YOU
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