IEEE Fourth International Conference on

Biometrics: Theory, Applications and Systems

Invited Speakers

Dr. Matthew Turk - Grand Challenges In Biometrics. (pdf of slides).

Abstract. The area of biometrics research has developed into a mature field with important theoretical results, working systems, and market demand. I general, however, the promise of biometric systems that are ubiquitous, reliable, adaptable, robust, unobtrusive, and easy to deploy is still far away. While good progress is being made in many areas, including exploring new modalities, multi-modal fusion, performance evaluation, and more useful data collection and sharing, it is useful to consider how these efforts may fit together with current and future needs, and how progress can be spurred to meet these needs. In this talk we will consider a set of grand challenges for the community and discuss the possible implications for directions in biometric research, funding, and industry.

Matt is a Professor in the Computer Science Department and Chair of the Media Arts and Technology Graduate Program at the University of California, Santa Barbara. His PhD thesis on Interactive Time Vision: Face Recognition as a Visual Behavior led to one of the seminal papers on biometrics: M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, 1991. His primary research interests are mostly concerned with using computer vision as an input modality. That means using cameras (and other sensors) to perceive relevant information about people - e.g., identity, facial expression, body movement, gestures - and then using this information to improve the perceptual interface between humans and computers. He will chair the ninth IEEE conference on Automatic Face and Gesture Recognition in 2011.

Dr. Samy Bengio - Large-Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings. (pdf of slides).

Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at k of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both the images and annotations. Our method both outperforms several baseline methods and, in comparison to them, provides a scalable architecture in terms of memory consumption and prediction time. We also demonstrate how our method learns an interpretable model, where annotations with alternate spellings or even languages are close in the embedding space. Hence, even when our model does not predict the exact annotation given by a human labeler, it often predicts similar annotations, a fact that we try to quantify by measuring the newly-introduced "sibling" precision method, where our method also obtains excellent results.

Samy Bengio is well known for his research in machine learning and his current research interests are in large scale large margin machine learning, mainly applied to images and sounds. He has been a research scientist at Google since early 2007. Before that, he was a senior researcher in statistical machine learning at the IDIAP Research Institute in Switzerland, where he supervised PhD students and postdoctoral fellows working on many areas of machine learning such as support vector machines, time series prediction, mixture models, large scale problems, speech recognition, multi channel and asynchronous sequence processing, multi-modal person authentication, brain computer interfaces, text mining, and many more. He is Associate Editor of the Journal of Computational Statistics, has been general chair of the Workshops on Machine Learning for Multimodal Interactions (MLMI'2004, 2005 and 2006), program chair of the IEEE Workshop on Neural Networks for Signal Processing (NNSP'2002), and on the program committee of several international conferences such as NIPS and ICML.

Dr. Tim Valentine - Confirmation Bias in Biometric and Forensic Identification. (pdf of slides).

Abstract. Confirmation bias is a tendency to interpret information in a way that confirms prior expectations. Recently it has been demonstrated that judgements by fingerprint experts can be influenced by contextual information. Biometric and forensic identification ultimately relies on human judgement. In this talk identification of facial images from CCTV by the courts will be considered. Human error in matching facial identity, the efficacy of facial comparison methods used by expert witnesses, and the potential influence of confirmation bias will be discussed.

Tim Valentine is a Professor of Psychology at Goldsmiths, University of London. He obtained his PhD from the University of Nottingham in 1986. After doctoral research at the MRC Applied Psychology Unit, Cambridge, he held lectureships at the Universities of Manchester and Durham before being appointed to a Chair at Goldsmiths in 1997. He has over 25 years research experience in human face recognition and eyewitness identification. Recent projects include work on police identification procedures, identification from CCTV and evaluation of a new method for generating facial composites. He has provided advice to the courts on identification issues in many criminal cases, including the appeal by Abdelbaset al Megrahi against his conviction for the Lockerbie bomb.

Dr. Jonathon Phillips - Face and Ocular Challenges. (pdf of slides).

Dr. Jonathon Phillips is a leading researcher in the fields of computer vision, biometrics, face recognition, and human identification. He is at the National Institute of Standards and Technology (NIST), where he is managing the Multiple Biometrics Evaluation 2010 and the Multiple Biometrics Grand Challenge. His previous efforts include the Iris Challenge Evaluations (ICE), the Face Recognition Vendor Test (FRVT) 2006 and the Face Recognition Grand Challenge and FERET. From 2000-2004, Dr. Phillips was assigned to the Defense Advanced Projects Agency (DARPA) as program manager for the Human Identification at a Distance program. He was test director for the FRVT 2002. For his work on the FRVT 2002 he was awarded the Dept. of Commerce Gold Medal. His work has been reported in print media of record including the New York Times and the Economist. He has appeared on NPR's ScienceFriday. Prior to joining NIST, he was at the US Army Research Laboratory. He received his Ph.D. in operations research from Rutgers University. From 2004 2008 he was an Associate Editor for the IEEE Trans. on Pattern Analysis and Machine Intelligence and guest editor of an issue of the Proceedings of the IEEE on biometrics. In an Essential Science Indicators analysis of face recognition publication over the past decade, Jonathon's work ranks at #2 by total citations and #1 by cites per paper. He is a Fellow of the IEEE and the IAPR.