2-5 June 2009 University of Sassari, Italy



The 3rd IAPR/IEEE International Conference on Biometrics




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Keynote Speakers

What can machine vision learn from human perception?

  Heinrich H. Bülthoff
    Max Planck Institute for Biological Cybernetics
    Tübingen, Germany

The question of how we learn to recognize and categorize objects and events has been at the heart of cognitive and neuroscience research for the last decades. Despite tremendous advances in the field of computational vision – made possible by the development of powerful learning techniques as well as the existence of large amounts of labeled training data for harvesting - artificial systems have yet to reach human performance levels and generalization capabilities. In this contribution we want to highlight some recent results from perceptual studies that could help to bring artificial systems a few steps closer to this grand goal.

In particular, we focus on the issue of spatio-temporal object representations (dynamic faces), face synthesis, as well as the need for taking into account multi-sensory data in models of object categorization. In all of these perceptual research lines, the underlying research philosophy was to combine the latest tools in computer vision, computer graphics, and computer simulations in order to gain a deeper understanding of recognition and categorization in the human brain. Conversely, we discuss how the perceptual results can feed back into the design of better and more efficient tools for artificial systems.

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40 years of progress in automatic speaker recognition technology

  Sadaoki Furui
   Department of Computer Science
   Tokyo Institute of Technology

This talk surveys the past 40 years of automatic speaker recognition (ASR) research, mainly focusing on activities conducted at our laboratory at NTT and Tokyo Tech. The research topics include: inter-session variability of speaker-dependent spectral features; normalization of the intersession variability; text-independent ASR by long-term averaged spectrum; DTW/HMM-based text-dependent ASR; delta- and delta-delta-cepstrum; VQ/HMM/GMM-based text-independent ASR;

text-prompted ASR; posterior probability-based normalization; noise-robust ASR; ASR by segmental and prosodic features; multi-modal ASR using lips, face, or ear images; stream-weight and threshold optimization for multi-stream ASR, and speaker diarization. Although significant technological progress has been achieved since the 1960's, there still exist a number of research issues that we need to tackle and solve.

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Lessons learned and challenges in large scale biometrics systems

  Jean-Christophe Fondeur
   R&T director for Sagem Sécurité

Jean-Christophe Fondeur is the R&T director for Sagem Sécurité. He has been leading Sagem's biometric research activity for more than 8 years, including 3 years as the chief scientist for Sagem Morpho Inc in the United States. He was involved in all of Sagem’s breakthroughs in biometrics of the past 15 years, such as the development of fingerprint recognition algorithms for the FBI AFIS system as well as for Match-On-Card applications, or the development of multi-biometrics for border crossing.

He holds more than 10 patents in the field of biometrics and graduated from the French schools “Ecole Polytechnique” and "Ecole Nationale Supérieure des Télécomunications de Paris".

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