Plenary Speakers

Plenary Speakers SOCO'13


Date: 11th September, 2013

Hujun Yin

Prof. Hujun Yin



The University of Manchester, UK.
Email: h.yin@manchester.ac.uk

 

 

 

 

 

Web: http://personalpages.manchester.ac.uk/staff/hujun.yin/

Abstract  

Among various biometric methods, facial image based has certain advantages such as nonintrusive, easily deplorable and highly accurate. Face recognition has found increased applications in security, surveillance, authentication, e-commerce and social networking. Most existing facial recognition methods are appearance-based and reply on facial images for training and recognition. There also exist methods for extracting geometric features of facial images and local facial features for improving recognition performance and/or reducing processing time. This talk gives an introduction to face recognition and its fundamental methods, implementations and applications. The talk will also discuss the challenges in implementation and practical systems.

Face recognition can be cast as a pattern classification and machine learning problem: given a set of facial images labeled with subjects’ identities, a classifier is developed or trained; so that when a novel face image from the same group of people (or not) is presented. That is, we seek to identify it from the database. Usually face recognition process involves three steps: face detection, feature extraction and classification. In the first step, the face is detected and located in the image. In the second step, a collection or combination of descriptive measurements or features are extracted from each image. In the third step, a classifier is trained on known samples to assign to each feature vector with subject’s identity. Advanced feature extraction methods such as local binary pattern (LBP) and active shape will also be discussed, along with the roles of advanced feature selection or dimensionality reduction methods and advanced classification techniques such as Support Vector Machines (SVM) or kernel methods.

Then the talk will discuss the challenges facing various aspects of face recognition systems. Lighting effect is far by the largest problem in face recognition or many image processing and computer vision. Solutions from homomorphic filtering to low dimensional representation (e.g. spherical harmonics and manifolds) will be explained. Other challenges include robust or invariant to pose, expression and partial occlusion. Increasing popularity and imaging power of mobile devices such as smartphones and tablets has prompted development of numerous new applications on them. Implementing face recognition onto mobile devices poses a new challenge as the application environment is subject to various uncertainties such as lighting, background and viewing angles, as well as reduced computational power. 


Date: 12th September, 2013

Manuel Graña

Prof. Manuel Graña

Manuel Graña is currently full professor at the Computer Science and Artificial Intelligence of the University of the Basque Country (UPV/EHU), in the Facultad de Informatica in San Sebastian. His research interests include Machine Learning and Patter Recognition, Medical Image Processing and Computer Aided Diagnosis systems, Mobile Robot Navigation, Multi-Agent Systems with natural inspiration, Social Network innovations via Computational Intelligence. The development of Lattice Computing approaches in those domains is his principled research endeavor. He is member of MIR Labs, IEEE and ACM. He has chaired three international conferences (IWANN 2007, HAI 2010, KES 2012). He has been editor of more than ten books. He has been advisor of more than twenty successful doctoral students. He has coauthored over one hundred papers in impact research journals.

Abstract

This talk reviews a taxonomy of social computation issues, from the computational social science to some forms of social computing. The aim is to give a view of social interaction as intelligent computing phenomena. The proposal of subconscious social intelligent computing entails a hidden intelligent layer, which learns from user interaction while solving problems in order to propose innovative and customized solutions. The design of such systems opens a broad avenue of research and application for hybrid artificial intelligent systems, involving diverse areas such as innovation generation, trust computing, reinforcement learning, user modelling, internet of things, etc.

 


Date: 13th September, 2013

Hujun Yin

Prof. Hojjat Adeli

Prof. Hojjat Adeli

Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the age of 26. He has authored over 500 research and scientific publications in various fields of computer science, engineering, applied mathematics, and medicine including 15 books such as Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems (Wiley, 1995); Wavelets in Intelligent Transportation Systems (Wiley, 2005); Intelligent Infrastructure (CRC Press, 2009), Automated EEG-based Diagnosis of Neurological Disorders - Inventing the Future of Neurology (CRS Press, 2010), and most recently Computational Intelligence - Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing (Wiley 2013); In 1998 he received the Distinguished Scholar Award from The Ohio State University “in recognition of extraordinary accomplishment in research and scholarship”. In 2007, he received the OSU College of Engineering Peter L. and Clara M. Scott Award for Excellence in Engineering Education as well as the Charles E. MacQuigg Outstanding Teaching Award.” He is the Editor-in-Chief of the International Journal of Neural Systems, Integrated Computer-Aided Engineering, and Computer-Aided Civil and Infrastructure Engineering. He is a Distinguished Member of ASCE and a Fellow of American Association for the Advancement of Science and IEEE.

 

 

Contact information:
College of Engineering
The Ohio State University
470 Hitchcock Hall, 2070 Neil Avenue
Columbus, OH 43210 U.S.A.
Voice and Fax: 614-292-7929
Email: Adeli.1@osu.edu

Abstract

From Automated EEG-Based Diagnosis of the Neurological and Psychiatric Disorders to Brain-Computer Interface

In this keynote lecture, the author first presents a novel multi-paradigm methodology for automated electroencephalogram (EEG)-based diagnosis of neurological and psychiatric disorders. The methodology is based on adroit integration of three different computing technologies and problem solving paradigms: neural networks, wavelets, and the chaos theory. Examples of the research performed by the author and his associates for automated diagnosis of epilepsy, the Alzheimer’s Disease, Attention Deficit Hyperactivity Disorder (ADHD), and autism spectrum disorder (ASD) are reviewed briefly. Next, extension of the research into development of a brain-computer interface is discussed. The lecture ends with an outline of research on reading human thought-processes  

 

 

eXTReMe Tracker