Anil K. Jain
Michigan State University, USA
Abstract: A central question in fingerprint recognition is the identification of fingermarks, also known as latent prints, from crime scenes. This requires searching for a fingermark’s mate in large databases of reference prints (rolled and slap prints) to identify suspect(s). The first reported use of fingermarks was in the trial of a suspect in a homicide investigation in Argentina in 1893. Ever since, fingermarks have been the most widely used source of evidence in forensic agencies worldwide. The FBI’s NGI system, with the largest criminal database (~80 million records) in the world, conducted over 20,000 fingermark searches during the month of February 2017 alone. However, fingermark recognition continues to be a thorny problem due to the small friction ridge area, poor ridge clarity, and background noise in the fingerprint pattern. The best performing COTS AFIS in 2012 NIST ELFT-EFS achieved a hit rate of only 70.2%. Research on this topic is further hampered due to lack of access to (i) a COTS AFIS for benchmarking, and (ii) fingermark databases (with mates) for training and evaluation. In an effort to solve the aforementioned difficulties and push state-of-the-art in fingermark recognition, we present an open-source AFIS and benchmark it with COTS AFIS for fingermarks.
Bio: Anil Jain is a distinguished professor of Computer Science at Michigan State University. His research interests include pattern recognition, computer vision and biometric recognition. He is a Fellow of the ACM and IEEE and is a recipient of Guggenheim, Humboldt, Fulbright, and King-Sun Fu awards. He served as editor-in-chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence. Jain was a member of the United States Defense Science Board, Forensic Science Standards Board and AAAS latent fingerprint working group. He was elected to the National Academy of Engineering and the Indian National Academy of Engineering.
Deakin University, Australia
Delivering efficiencies in health care and manufacturing
Abstract: This talk considers what to do when confronted with failures with current data or analysis. What can we do
when current predictions for rare events are poor?
Instead of focusing on rare event classification, for example, suicide prediction, we focus on identifying the riskiest events with minimal error. Such events are likely precursors to outliers of interest. We demonstrate our results through outlier detection in surveillance (leading to our start-up company iCetana, Australia) and in suicide risk prediction (implemented in in Barwon Health, Geelong, Australia). We discuss the challenges in data modeling, pitfalls and our outcomes.
when data has special characteristics?
We predict cancer toxicity risk, and show how we leverage the special characteristics of the data to build better predictive models. We share our insights we have learnt in our path from such data to models.
when data is limited?
We use Bayesian optimisation based methods to demonstrate how to accelerate the experimental process, the foundation of both product ad process design. We show how we have been able to impact the discovery of novel materials and alloys.
Bio: Professor Venkatesh and her team have tackled a wide range of problems of societal significance, including the critical areas of autism, security and aged care. The outcomes have impacted the community and evolved into publications, patents, tools and spin-off companies. This includes 554 publications, 3 full patents, 3 start-up companies (iCetana.com, Virtual Observer.com, iHosp) and a significant product (TOBY Playpad).
Professor Venkatesh has tackled complex pattern recognition tasks by drawing inspiration and models from widely diverse disciplines, integrating them into rigorous computational models and innovative algorithms. Her main contributions have been in the development of theoretical frameworks and novel applications for analyzing large scale, multimedia data. This includes development of several Bayesian parametric and non-parametric models, solving fundamental problems in processing multiple channel, multi-modal temporal and spatial data.
Australian Passport Office, Australia
Bio: Stephen has been an Assistant Secretary in the Australian Passport Office since December 2016.
Stephen entered the Department of Foreign Affairs and Trade as a graduate in 1990. He served for three years on the Hong Kong and Taiwan desk before postings to Dhaka and Bonn. Returning to Canberra, he worked in the Executive Branch, as director of diplomatic security, and as the director responsible for relations with Thailand, Vietnam and Laos.
From 2004 to 2007, as political and economic counsellor in Bangkok, Stephen faced challenges that included the Asian tsunami, a new bilateral free trade agreement and the 2006 coup. Returning to Canberra, he spent six years working on relations with the Pacific islands, culminating in five months as acting head of the department’s Pacific Division. From 2013 to 2016, Stephen headed the Political Branch at Australia’s Embassy in Washington DC.