Keynote speakers

Arun Ross:
Biometrics in the Wild: Heterogeneous Face and Iris Recognition 

image1Arun Ross is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University (MSU) and the Director of the i-PRoBe Lab. Prior to joining MSU in 2013, he was a faculty member at West Virginia University (WVU) from 2003 to 2012. He also served as the Assistant Site Director of the NSF Center for Identification Technology and Research (CITeR) between 2010 and 2012. Arun received the B.E. (Hons.) degree in Computer Science from the Birla Institute of Technology and Science, Pilani, India, and the M.S. and Ph.D. degrees in Computer Science and Engineering from Michigan State University. He is the coauthor of the textbook “Introduction to Biometrics” and the monograph “Handbook of Multibiometrics,” and the co-editor of “Handbook of Biometrics”. He is a recipient of the IAPR JK Aggarwal Prize, the IAPR Young Biometrics Investigator Award (YBIA), the NSF CAREER Award, and was designated a Kavli Frontier Fellow by the National Academy of Sciences in 2006. He also received the 2005 Biennial Pattern Recognition Journal Best Paper Award. Arun served as a panelist at a counter-terrorism event that was organized by the United Nations Counter-Terrorism Committee (CTC) at the UN Headquarters in May 2013. He was an Associate Editor of IEEE Transactions on Information Forensics and Security (2009 – 2013), and IEEE Transactions on Image Processing (2008 – 2013). He currently serves as Area Editor of the Computer Vision and Image Understanding Journal, Associate Editor of the Image and Vision Computing Journal, and Chair of the IAPR TC4 on Biometrics. URL: http://www.cse.msu.edu/~rossarun/

 

Stefanos Zafeiriou:
Advances in Active Appearance Models for Face Alignment in-the-wild

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Stefanos Zafeiriou: Stefanos Zafeiriou is a Lecturer (equivalent to Assistant Professor) in Pattern Recognition/Statistical Machine Learning for Computer Vision in the Department of Computing, Imperial College London. He has been awarded one of the prestigious Junior Research Fellowships (JRF) from Imperial College London in 2011 to start his own independent research group. He is/has participated in more than 10 EU, British and Greek research projects. Dr. Zafeiriou currently serves as an Associate Editor in IEEE Transactions on Cybernetics and Image and Vision Computing journal. He has been guest editor in more than four special issues and co-organized more than five workshops/ special sessions in top venues such as CVPR/FG/ICCV/ECCV. He has co-authored more than 40 journal papers mainly on novel statistical machine learning methodologies applied to computer vision problems such as 2D/3D face and facial expression recognition, deformable object tracking, human behaviour analysis etc published in the most prestigious journals in his field of research (such as IEEE T-PAMI, IJCV, IEEE T-IP, IEEE T-NNLS, IEEE T-VCG, IEEE T-IFS etc). His students are frequent recipients of very prestigious and highly competitive fellowships such as Google Fellowship, Intel Fellowship and the Qualcomm fellowship. He has more than 1600 citations to his work and an h-index of 23.

Abstract: The past few years we witnessed tremendous development in face landmark localisation and tracking in arbitrary recording conditions (also referred to as “in-the-wild”). This progress can be attributed to the following factors: (a) the efforts made by the scientific community to collect and consistently annotate “in-the-wild” facial images, (b) the design and use of robust features such as SIFT, HoG etc., (c) the advancements made in various facial deformable model architectures. In this talk, I will discuss about recent advances in the most popular, arguably, statistical deformable model, i.e. the so-called Active Appearance Models (AAMs). Methods based on AAMs, contrary to what was believed a few years ago, are still among the state-of-the-art facial landmark localisation methods, with their main advantage being that they require only few images to be robustly trained.