Invited speakers

May 12
Bhaskar D. Rao
IEEE SP Distinguished Lecturer
Bayesian methods for
sparse signal recovery 		    May 13
Alex C. Kot
IEEE SP Distinguished Lecturer
Is your biometric data safe?

Bayesian methods for sparse signal recovery

[slides]

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem.

Is your biometric data safe?

[slides]

Nowadays, biometrics is widely used in authentication systems. In general, biometrics needs to be stored in a database for subsequent authentication. However, templates stored in the database are at the risk of being stolen or modified. Once the template is stolen, it is difficult to be replaced like passwords and the private user information associated with the stolen template would also be exposed. Thus, biometrics templates should be stored in the database such that both the security of the template and the privacy of the user are not compromised under various attacks. This talk will cover some existing techniques in dealing with biometrics data protection. New schemes in creating new fingerprint based on two sets of minutiae from two different fingers will be presented and a novel data hiding scheme is also proposed for the thinned fingerprint template.

Bhaskar D. Rao received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in 1979 and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1981 and 1983, respectively. Since 1983, he has been with the University of California at San Diego, La Jolla, where he is currently a Professor with the Electrical and Computer Engineering. He is the holder of the Ericsson Endowed Chair in Wireless Access Networks and was the Director of the Center for Wireless Communications (2008-2011).

Prof. Rao was elected IEEE Fellow in 2000 for his contributions to the statistical analysis of subspace algorithms for harmonic retrieval. His work has received several paper awards; Best Paper Award (2013) for the paper "Multicell Random Beamforming with CDF-based Scheduling: Exact Rate and Scaling Laws"; SPS Best Paper Award (2012) for the paper "An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem"; Stephen O. Rice Prize Paper Award in the Field of Communication Systems (2008) for the paper "Network Duality for Multiuser MIMO Beamforming Networks and Applications"; Best Paper Award (2000) for the paper "PDF Optimized Parametric Vector Quantization of Speech Line Spectral Frequencies".

Prof. Rao’s interests are in the areas of digital signal processing, estimation theory, and optimization theory, with applications to digital communications, speech signal processing, and biomedical signal processing.

Alex C. Kot received his BS in electrical engineering and MBA degrees both from the University of Rochester. He obtained his MS and PhD in electrical engineering from the University of Rhode Island. Prof. Kot has been with the Nanyang Technological University, Singapore since 1991. He headed the Division of Information Engineering with more than 40 faculty members at the School of Electrical and Electronic Engineering for eight years and served as Associate Chair/ Research and Vice Dean Research for the School of Electrical and Electronic Engineering. He is currently Professor and Associate Dean for the College of Engineering. He is the Director of Rapid-Rich Object SEarch (ROSE) Lab, partnering with Peking University.

Dr. Kot has served the IEEE SP Society in various capacities such as General Co-Chair, ICIP 2004 and Chairman, SPS Chapter Chairs. He served as Member, IEEE Fellow Evaluation Committee; Vice-President Finance, IEEE Signal Processing Society (2013-2014); Member, SPS Conference Board (2013-2014); and Member, SPS Publications Board (2013-2014). He received the Best Teacher of the Year Award and is a co-author for several Best Paper Awards including ICPR, IEEE WIFS, ICEC and IWDW. He was the IEEE CASS Distinguished Lecturer in 2005 and 2006 and is a Fellow of IES, a Fellow of IEEE, and a Fellow of Academy of Engineering, Singapore.

Dr. Kot has published extensively in the areas of signal processing for communication, biometrics, data-hiding, image forensics, information security. His new research area is in the domain object search and recognition.