2017 TPAMI Faces

Jeffrey Cohn

University of Pittsburgh, Pittsburgh, United States of America

jeffcohn@pitt.edu

Jeffrey Cohn is Professor of Psychology and Psychiatry at the University of Pittsburgh and Adjunct Professor of Computer Science at the Robotics Institute at Carnegie Mellon University. He leads interdisciplinary and inter-institutional efforts to develop advanced methods of automatic analysis and synthesis of facial expression and prosody; and applies those tools to research in human emotion, social development, non-verbal communication, psychopathology, and biomedicine. His research has been supported by grants from the U.S. National Institutes of Health, National Science Foundation, Autism Foundation, Office of Naval Research, and Defense Advanced Research Projects Agency among other sponsors. He has co-chaired the IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017, FG2015, and FG2008), the International Conference on Affective Computing and Intelligent Interaction (ACII 2009), and the International Conference on Multimodal Interfaces (ACM 2014). He is a co-editor of IEEE Transactions in Affective Computing (TAC) and has co-edited special issues on affective computing for the Journal of Image and Vision Computing, Pattern Recognition Letters, Computer Vision and Image Understanding, and ACM Transactions on Interactive Intelligent Systems.

Sergio Escalera

Computer Vision Center (UAB) and University of Barcelona, Spain

sergio.escalera.guerrero@gmail.com

Sergio Escalera is Full Professor at the Department of Mathematics and Informatics, Universitat de Barcelona, where he is the head of the Informatics degree. He is ICREA Academia. He leads the Human Pose Recovery and Behavior Analysis Group. He is Distinguished Professor at Aalborg University. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is also Fellow of the ELLIS European Laboratory for Learning and Intelligent Systems working within the Human-centric Machine Learning program. He participated in several international funded projects and received an Amazon Research Award. He has published more than 300 research papers and received a CVPR best paper award nominee and a CVPR outstanding reviewer award.

Hugo Jair Escalante

INAOE, México

hugojair@inaoep.mx

Hugo Jair Escalante is researcher scientist at Instituto Nacional de Astrofisica, Optica y Electronica, INAOE, Mexico. Previously, he was assistant professor at the Graduate Program on Systems Engineering at UANL. He holds a PhD in Computer Science, for which he received the best PhD thesis on Artificial Intelligence 2010 award (Mexican Society in Artificial Intelligence). He was granted the best paper award of the International Joint Conference on Neural Networks 2010 (IJCNN2010). He is secretary and member of the board of directors of ChaLearn, The Challenges in Machine Learning Organization, a non-profit organism dedicated to organizing challenges, since 2011. Also, he is member of the board of the CONACYT Network on Applied Computational Intelligence, regular member of AMEXCOMP and member of the National System of Researchers (SNI). Since 2017, he is editor of the Springer Series on Challenges in Machine Learning, a new book series focused on academic competitions within machine learning and related fields. He has been involved in the organization of several challenges in computer vision and machine learning, collocated with top venues in machine learning and computer vision, see http://chalearnlap.cvc.uab.es/. He has served as co-editor of special issues in IJCV, IEEE TPAMI, and IEEE Transactions on Affective Computing. He has served as area chair for NIPS 2016 and NIPS 2017, and has been member of the program committee of venues like CVPR, ICPR, ICCV, ECCV, ICML, NIPS, IJCNN. His research interests are on machine learning, evolutionary computing and its applications on language and vision.

Maja Pantic

Imperial College, London, United Kingdom

m.pantic@imperial.ac.uk

Maja Pantic is a Professor of Affective and Behavioral Computing in the Imperial College and leader of the i·BUG group, working on machine analysis of human non-verbal behaviour and its applications to humancomputer, human-robot, and computer-mediated human-human interaction. Prof. Pantic published more than 200 technical papers in the areas of machine analysis of facial expressions, machine analysis of human body gestures, audiovisual analysis of emotions and social signals, and humancentered machine interfaces. She has more than 12,000 citations to her work, and has served as the Key Note Speaker, Chair and Co-Chair, and an organization/ program committee member at numerous conferences in her areas of expertise.

Michel Valstar

University of Nottingham, Nottingham, United Kingdom

michel.valstar@nottingham.ac.uk

Michel Valstar is an Associate Professor at the University of Nottingham, School of Computer Science, and a researcher in Automatic Visual Understanding of Human Behaviour. He is a member of both the Computer Vision Lab and the Mixed Reality Lab. Automatic Human Behaviour Understanding encompasses Machine Learning, Computer Vision, and a good idea of how people behave in this world.Dr Valstar is currently coordinator of the H2020 LEIT project ARIA-VALUSPA, which will create the next generation virtual humans. He has also recently been awarded a prestigious Melinda & Bill Gates foundation award to automatically estimate babies' gestational age after birth using the mobile phone camera, for use in countries where there is no access to ultrasound scans. Michel was a Visiting Researcher at the Affective Computing group at the Media Lab, MIT, and a research associate with the iBUG group, which is part of the Department of Computing at Imperial College London. Michel’s expertise is facial expression recognition, in particular the analysis of FACS Action Units. He recently proposed a new field of research called 'Behaviomedics', which applies affective computing and Social Signal Processing to the field of medicine to help diagnose, monitor, and treat medical conditions that alter expressive behaviour such as depression.

Xavier Baró

Universitat Oberta de Catalunya, Barcelona, Catalonia

xbaro@uoc.edu

Xavier Baró received his B.S. degree in Computer Science at the Universitat Autònoma de Barcelona (UAB) in 2003. In 2005 he obtained his M.S. degree in Computer Science at UAB, and in 2009 the Ph.D degree in Computer Engineering. At the present he is a lecturer and researcher at the IT, Multimedia and Telecommunications department at Universitat Oberta de Catalunya (UOC). He is involved on the teaching activities of the Computer Science, Telecommunication and Multimedia degrees of the UOC, and collaborates as assistant professor on the teaching activities of the Computer Science degree at the Applied Mathematics and Analysis of the Universitat de Barcelona (UB). In addition, he is involved on the Interuniversity master on Artificial Intelligence (UPCUBURV). He is cofounder of the Scene Understanding and Artificial Intelligence (SUNAI) group of the UOC, and collaborates with the Computer Vision Center of the UAB, as member of the Human Pose Recovery and Behavior Analysis (HUPBA) group. His research interests are related to machine learning, evolutionary computation, and statistical pattern recognition, specially their applications to generic object recognition over huge cardinality image databases.

Isabelle Guyon

University Paris-Saclay, France and ChaLearn USA

isabelle@clopinet.com

Isabelle Guyon ( http://guyon.chalearn.org/ ) is chaired professor in “big data” at the Université ParisSaclay, specialized in statistical data analysis, pattern recognition and machine learning. She is one of the cofounders of the ChaLearn Looking at People (LAP) challenge series and she pioneered applications of the MIcrosoft Kinect to gesture recognition. Her areas of expertise include computer vision and and bioinformatics. Prior to joining ParisSaclay she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and coinvented with Bernhard Boser and Vladimir Vapnik Support Vector Machines (SVM), which became a textbook machine learning method. She worked on early applications of Convolutional Neural Networks (CNN) to handwriting recognition in the 1990’s. She is also the primary inventor of SVMRFE, a variable selection technique based on SVM. The SVMRFE paper has thousands of citations and is often used as a reference method against which new feature selection methods are benchmarked. She also authored a seminal paper on feature selection that received thousands of citations. She organized many challenges in Machine Learning since 2003 supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie, Paris, France. She is president of Chalearn, a nonprofit dedicated to organizing challenges, vicepresident of the Unipen foundation, adjunct professor at NewYork University, action editor of the Journal of Machine Learning Research, editor of the Challenges in Machine Learning book series of Microtome, and program chair of the upcoming NIPS 2016 conference.