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FERA 2017 will be held on 3 June 2017, in Room 3 of the same venue as the main FG conference (Double Tree by Hilton Washington DC – Crystal City Double Tree Crystal City).

*** Lunch: 12.30-14.00

Keynote Speaker
14:00 – 15:00 Keynote: Fernando de La Torre

Session 1
15:00 – 15:15 Lijun Yin; Michel Valstar; Laszlo A Jeni; Jeffrey Cohn; Maja Pantic; Jeffrey M Girard; Enrique Sánchez-Lozano*; Zheng Zhang, ‘FERA 2017 – Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge’
15:15 – 15:30 Jun He; Dongliang Li; Bin Yang; Siming Cao; Bo Sun; Lejun Yu, ‘Multi View Facial Action Unit Detection based on CNN and BLSTM-RNN’

*** Coffee break: 15.30-16.00

Session 2
16:00 – 16:15 Markus Kächele; Mohammadreza Amirian; Günther Palm; Friedhelm Schwenke, ‘Support Vector Regression of Sparse Dictionary-based Features for View-Independent Action Unit Intensity Estimation’
16:15 – 16:30 Qin Jin; Xinrui Li; Shizhe Chen, ‘Facial Action Units Detection with Multi-Features and -AUs Fusion’
16:30 – 16:45 Olga Bellon; Luciano Silva; Júlio C Batista; Vítor Albiero, ‘AUMPNet: simultaneous Action Units detection and intensity estimation on multipose facial images using a single convolutional neural network’
16:45 – 17:00 Jimin Pi; Bertram Shi; Yuqian Zhou, ‘Pose-independent Facial Action Unit Intensity Regression Based on Multi-task Deep Transfer Learning’
17:00 – 17:15 Chuangao Tang; Jingwei Yan; Wenming Zheng; Zhen Cui; Yang Li; Qiang Li; Tong Zhang, ‘View-Independent Facial Action Unit Detection’

17:15 – 17:30 Announcement of winners & closing statements

Keynote: ‘Computational Face’ by Fernando De la Torre


The face is one of the most powerful channels of nonverbal communication Facial expression provides cues about emotion, intention, alertness, pain, personality, regulates interpersonal behavior, and communicates psychiatric and biomedical status among other functions. Within the past 30 years, there has been increasing interest in automated methods for facial image analysis from video. In this talk, I will discuss recent advances in machine learning techniques for facial expression analysis. In particular, I will review recent computational face models for facial feature detection, algorithms for supervised facial expression detection (e.g., personalization of facial classifiers, early facial event detection, sample selection for action unit detection), and unsupervised methods for facial behavior analysis.


Fernando De la Torre received his B.Sc. degree in Telecommunications (1994), M.Sc. (1996), and Ph. D. (2002) degrees in Electronic Engineering from La Salle School of Engineering in Ramon Llull University, Barcelona, Spain. In 2003 he joined the Robotics Institute at Carnegie Mellon University, and since 2010 he has been a Research Associate Professor, where he leads the Human Sensing Laboratory (http://humansensing.cs.cmu.edu). In 2014 he founded FacioMetrics LLC that was acquired by Facebook in 2016. Currently, he is research scientist manager at Facebook. Dr. De la Torre’s research interests include computer vision and machine learning, in particular face analysis, optimization and component analysis methods, and its applications to human sensing. He is Associate Editor at IEEE PAMI and has won the best student paper award at IEEE Computer Vision and Pattern Recognition 2012.