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Project 10 Facial Emotion Recognition Based On Mouth Analysis

Project 10 Facial Emotion Recognition Based On Mouth Analysis. SSIP 08, Vienna. http://www.we-hope-project10-will-win.info. The Project. Objective : To recognize emotional state / expression using mouth information Input: Mouth images (no make-up) Output: Emotional State/ Expression

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Project 10 Facial Emotion Recognition Based On Mouth Analysis

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  1. Project 10Facial Emotion Recognition Based On Mouth Analysis SSIP 08, Vienna http://www.we-hope-project10-will-win.info

  2. The Project • Objective : To recognize emotional state / expression using mouth information • Input: Mouth images (no make-up) • Output: Emotional State/ Expression Happy, Neutral, Sad http://www.we-hope-project10-will-win.info

  3. The Team Péter Webprogrammer Sofia programmer Kornél programmer Naiem researcher Kamal programmer http://www.we-hope-project10-will-win.info

  4. The Tasks • Create facial expressions photographic database • Segment the mouth in the input image • Use suitable features for expression characterization • Design a reliable classifier to distinguish between different mouth expressions http://www.we-hope-project10-will-win.info

  5. SSIP Lips database Happy, Neutral and Sad Photos of SSIP students and lecturers Thank you all!!! Happy Neutral Sad http://www.we-hope-project10-will-win.info

  6. Thresholding Input Image HSV Space - Hue Morphological Operations Mouth Segmentation http://www.we-hope-project10-will-win.info

  7. Segmentation Results… And Segmentation Problems… http://www.we-hope-project10-will-win.info

  8. Lips Features Extraction Detect the leftmost and rightmost lip points Normalize images (rotation, translation and scaling) Calculate features Eccentricity Convex Area Minor Axis Ratio of Upper to Lower Lip http://www.we-hope-project10-will-win.info

  9. Expression Classification SVM Classifier Two Stage Classification  ☺ Mouth Features http://www.we-hope-project10-will-win.info

  10. Results 1 Differences between different classes were found to be statistically significant (p<0.01) Classification Accuracy Stage 1 (Sad / Not Sad)  88% Stage 2 (Happy/ Neutral) 62% http://www.we-hope-project10-will-win.info

  11. Results 2 http://www.we-hope-project10-will-win.info

  12. Conclusion • Mouth information is often insufficient for recognizing facial expression / emotional state • Other face features such as eyes and eyebrows can contribute in emotional state recognition Future Work • Acquire larger database for training and testing • Test different facial expressions (such as anger and disgust) • Other classifiers: NN, FIS http://www.we-hope-project10-will-win.info

  13. GUI http://www.we-hope-project10-will-win.info

  14. References M. Gordan, C. Kotropoulos, I. Pitas, “Pseudoautomatic Lip Contour Detection Based on Edge Direction Patterns” J. Kim, S. Na, R. Cole, “Lip Detection Using Confidence-Based Adaptive Thresholding” F. Tang, “Facial Expression Recognition using AAM and Local Facial Features” M. Pantic, M. Tomc, L. Rothkrantz , “A Hybrid Approcah to Mouth Features Detection” http://www.we-hope-project10-will-win.info

  15. Thank you for your attention!!! http://www.we-hope-project10-will-win.info

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