[Skolkovo Robotics V] Facial Expression Recognition in the Wild
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Презентация д-ра Адил Хана из Университета Иннополис (Татарстан)
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[Skolkovo Robotics V] Facial Expression Recognition in the Wild
- 1. Facial Expression Recognition in the Wild Adil Khan Machine Learning & Knowledge Representation Lab Institute of AI & Robotics Innopolis University
- 2. About ME! Associate Prof. Innopolis University (Russia) Head of Machine Learning & Knowledge Representation Lab Machine Learning, Computer Vision, Pervasive Computing
- 3. Why FER? We are emotional beings! Happy → Pizza BUT Sad → Ice-cream
- 4. Why FER? Most Effective Way to Understand Emotions: A. Mehrabian, Nonverbal Communication. New Brunswick, NJ, USA: Aldine, 2007.
- 5. Two Types: Macro vs. Micro
- 6. Anger in Two Types!
- 7. FER: An Extensively Researched Area! Facial expression recognition using radial encoding of local Gabor features and classifier synthesis Compound facial expressions of emotion Rapid perceptual integration of facial expression and emotional body language Simulationist models of face-based emotion recognition Emotion recognition in human computer interaction Real-time Mobile Facial Expression Recognition System
- 8. What Challenges Still Remain?
- 9. State-of-the-Art When Tested on Naturalistic Data! FER System Accuracy on ND AH-ASM 50 ± 4.5% W-BPNN 62 ± 3.1% LDN-SVM 64 ± 2.9% CLM-SVM 60 ± 3.5% RLBP-NN 64 ± 1.7% PHOG-SVM 59 ± 5.6%
- 10. Samples from Naturalistic Dataset
- 11. Samples from Available Datasets for Training Static Facial Expressions in the Wild (SFEW) Dataset
- 12. State-of-the-Art FER is Not Ready for The Real World!
- 13. FER Pipeline Pre-Processing Face Detection Facial Components Feature Extraction Classification Recognized Expression Class Image or Video
- 14. Journey Begins With Data! https://link.springer.com/article/10.1007/s11042-016-4321-2
- 15. We Combine but Combine Intelligently! http://www.araya.org/wp/wp-content/uploads/2016/10/gan.png
- 16. Features: Are Very Important; Already Got Some! Two broad categories: 1. Geometric Features ASM, AAM 2. Appearance based features GF, LBP, LDA, HOGs, etc.
- 17. Feature Fusion Has Been Tried; Works to Some Extent 1. Simple concatenation 2. Spectral Embedding 3. Genetic Algorithms 4. Multi Kernel Learning 5. Ensemble of classifiers 6. etc. Fusion Shape Info Texture Info Fused Feature Vector
- 18. We use Deep Learning for Feature Fusion
- 19. Still Variance Might Creep In!
- 20. Two Level is Good, but Too Naive/Heuristic! http://uclab.khu.ac.kr/resources/publication/J_201.pdf
- 21. We combine: One Network for Both Tasks! A Deep Network Cluster learning at the first layers Representation Learning within each cluster at the subsequent layers Classification at the last layer But have to figure out how to backprop
- 22. We Test on the REAL Data! The biggest challenge, but we are trying We collect our own real, spontaneous expression data 1. Online resources 2. Offline resources
- 23. Machine learning and Knowledge Representation Lab a.khan@innopolis.ru