In recent years, Deep Learning (DL) has become a dominant method for a wide variety of computer vision tasks. One of its biggest successes has been in face recognition where the performance has been improved dramatically. So, will DL make other face recognition algorithms obsolete? Is deep learning always the best solution in any scenarios? Is it necessary for researchers to deeply investigate the traditional face recognition technique in the DL era? Actually, deep learning is not perfect. For instance, deep learning heavily depends on big data which is sometimes quite expensive and sometimes may not be available. Due to this limitation, conventional methods achieve superior or comparable performance against DL methods in the field of facial landmark detection, face recognition across large poses, thermal/near Infrared Face Recognition, 3D face recognition, etc. It would be interesting to explicitly compare DL methods with traditional methods in terms of accuracy, efficiency and model complexity. We aim to investigate the scenarios where conventional methods can outperform DL methods by EXPLICIT comparison and deep analysis.
We welcome submissions on topics related to the invesigation of (i) the advantages of traditional methods against DL methods and (ii) insights into the reasons of these advantages. Submissions will be peer-reviewed and should follow the standard FG2018 IEEE format.
Prof. Neil Robertson (N.Robertson@qub.ac.uk ) is Professor of Research for Image and Vision Systems, ECIT, Queen's University Belfast, and the founding CTO of AnyVision, an international company specialising in face recognition for security and surveillance. His principal research interest is human activity recognition in video, face recognition and person re-identification in video.
Prof. Josef Kittler (email@example.com ) is Professor of Machine Intelligence at the Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, U.K. He conducts research in biometrics, video and image database retrieval, medical image analysis, and cognitive vision. He published a Prentice Hall textbook Pattern Recognition: A Statistical Approach and over 170 journal papers. Dr. Kittler serves on the Editorial Board of several journals and biometrics conferences such as ICB, BTAS, IJCB and FG.
Prof. Stan Z Li (firstname.lastname@example.org) is Professor and the Director of the Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He was an Associate Professor with Nanyang Technological University, Singapore. He was a Researcher with Microsoft Research Asia from 2000 to 2004. He has published over 200 papers in international journals and conferences on biometrics, and authored and edited eight books. His current research interests include pattern recognition and machine learning, image and vision processing, face recognition, biometrics, and intelligent video surveillance.
Dr. Zhen Lei (Zhen.email@example.com) is an Associate Professor with the Institute of Automation, Chinese Academy of Sciences. He haspublished over 90 papers in international journals and conferences. His current research interests include computer vision, pattern recognition, image processing, and face recognition in particular. Dr. Lei served as Area Chair of the International Joint Conference on Biometrics in 2014, the IAPR/IEEE International Conference on Biometric in 2015, and the IEEE International Conference on Automatic Face and Gesture Recognition in 2015.
Dr. Guosheng Hu (firstname.lastname@example.org) is a senior research scientist in AnyVision. He achieved his PhD degree in CVSSP, University of Surrey in 2015. He conducted postdoctoral research on face recognition using deep learning in LEAR, INRIA, Grenoble from 2015 to 2016. He joined AnyVision in June 2016. His research interests include face recognition using deep learning, 3D-aided face recognition, 3D face reconstruction. He has published 20+ research papers on ICCV, ECCV, PR, TIP, etc.
The 13th IEEE International Conference on AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2018) is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes: advances in fundamental computer vision, pattern recognition and computer graphics; machine learning techniques relevant to face, gesture, and body motion; new algorithms and applications.