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© Imperial College LondonPage 1 FERA2011: The First Facial Expression Recognition and Analysis Challenge FG’11 March 2011 Michel Valstar, Marc Méhu, Marcello.

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Presentation on theme: "© Imperial College LondonPage 1 FERA2011: The First Facial Expression Recognition and Analysis Challenge FG’11 March 2011 Michel Valstar, Marc Méhu, Marcello."— Presentation transcript:

1 © Imperial College LondonPage 1 FERA2011: The First Facial Expression Recognition and Analysis Challenge FG’11 March 2011 Michel Valstar, Marc Méhu, Marcello Mortillaro, Maja Pantic, Klaus Scherer

2 Participation overview Data downloaded by 20 teams 15 submissions 11 accepted papers 13 teams in Emotion Sub-Challenge 5 teams in AU Sub-Challenge Institutes from 6 countries 53 researchers, median of 6 per paper 5 entries were multi-institute endeavours © Imperial College LondonPage 2

3 Trends © Imperial College LondonPage 3 Machine Learning trends: 13/15 teams used SVM Three teams used multiple kernel SVMs, including the AU winner Only 1 team modelled time Only 1 team used probabilistic graphical models Feature trends: 4 teams encode appearance dynamics 4 teams use both appearance and geometric features (including AU winners) Only 1 team infers 3D, but appears successful! (AU winner) Only 1 team uses Geometric features only, ranked 11 th

4 Baseline System – LBP based Expression Recognition © Imperial College LondonPage 4 Face is registered using detected eyes. Uniform Local Binary Pattern features are computed on every pixel (LBP). Face is divided in 10x10 blocks. In each block a 256 bin histogram of the LBP features is generated. For every AU a GentleBoost-SVM is learned. Upper face AUs use the concatenated histograms of the top five rows, Lower face AUs the bottom five rows. For every Emotion a GentleBoost-SVM is learned using all rows. SVM predictions are per frame, decision is made by voting. Local Binary Pattern appearance descriptors are applied to the face region to detect AUs and discrete emotions

5 Baseline Overview (LAUD) © Imperial College LondonPage 5 B. Jiang, M.F. Valstar, and M. Pantic, “Action Unit detection using sparse appearance descriptors in space-time video volumes”, FG’11

6 Winner of the Emotion Detection sub- challenge 3. Karlsruhe Institute of Technology Tobias Gehrig, Hazim Ekenel © Imperial College LondonPage 6 2. UIUC-UMC Usman Tariq, Xi Zhou, Kai-Hsiang Lin, Zhen Li, Zhaowen Wang, Vuang Le, Thomas Huang, Tony Han, Xutao Lv 1. University of California, Riverside Songfan Yang, Bir Bhanu

7 Ranking – Emotion Sub-challenge © Imperial College LondonPage 7

8 Person independent/specific emotion detection © Imperial College LondonPage 8

9 Emotion secondary test results © Imperial College LondonPage 9

10 Winner of the Action Unit Detection sub- challenge 3. Karlsruhe Institute of Technology Tobias Gehrig, Hazim Ekenel © Imperial College LondonPage University of California San Diego Nicholas Butko, Javier Movellan, Tingfan Wu, Paul Ruvolo, Jacob Whitehill, Marian Bartlett 1. University of French West Indies & Guyana Lionel Prevost, Thibaud Senechal, Vincent Rapp, Hanan Salam, Renaud Seguier, Keving Bailly

11 Ranking – Action Unit Sub-challenge © Imperial College LondonPage 11

12 Person independent/specific AU detection © Imperial College LondonPage 12

13 Conclusion and new goals Conclusions: Person dependent discrete emotion detection is incredibly successful Dynamic appearance is very successful Combined appearance/geometric approaches seem to be the way forward AU detection far from solved © Imperial College LondonPage 13 New avenues: Given the high success of discrete emotion, dimensional affect may be a new goal to pursue Explicitly detecting temporal segments of facial expressions Analyse sensitivity of approaches to AU intensities. Leverage person specific approaches for AU detection Detection of AU intensity levels


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