Biometric identification systems typically require one-to-many comparisons by evaluating the biometric similarity between an input query and the database records. With increasing data volume and access demand, it is necessary to develop effective and efficient methods that can help to narrow the search range and reduce the matching complexity for computationally intensive tasks such as identity de-duplication and identification based on secured templates. Biometric indexing is designed to address the problem by assigning an index vector to every identity in the database, with the aim of fast retrieving a small number of candidate identities for matching. This talk will discuss problems and recent developments of biometric indexing from three aspects, namely, accuracy, efficiency and privacy. In particular, examples of hash-based indexing methods will be provided for biometric identification.
Yi Wang is a Research Assistant Professor with the Department of Computer Science, Hong Kong Baptist University. She received her PhD in Computer Science from RMIT University, Australia, in 2009. From 2009 to 2012, she was a Research Associate with the School of Mathematics and Statistics, The University of New South Wales, Australia. Her research has been largely focused on model-based methods for high-dimensional data anlaysis with applications in biometric identification and eco-statistics. According to ISI Web of Science, since 2007, her work has received more than 510 SCI citations from over 40 countries/territories.
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