Abstract
Person identification is a well studied problem in the last two decades. In a typical automated person identification scenario, the system always contains the prior knowledge, either person-based model or reference mugshot, of the person-of-interest. However, the challenge of automated person identification would increase by multiple folds if the prior information is not available. In today’s world, rich and large quantity of information are easily attainable through the Internet or closed-loop surveillance network. This provides us an opportunity to employ an automated approach to perform person identification with minimum prior knowledge, presume that there are sufficient amount of observations. In this paper, we propose a dominant set based person identification framework to learn the identity of a person through large-scale observations, where each observation contains instances from various modality. Through experiments on two challenging face datasets we show the potential of the proposed approach. We also explore the conditions required to obtain satisfy performance and discuss the potential future research directions.
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Acknowledgment
This research was carried out at the NUS-ZJU Sensor-Enhanced Social Media (SeSaMe) Centre. It is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the Interactive Digital Media Programme Office.
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Wong, Y., Chaisorn, L., Kankanhalli, M.S. (2015). Discovering Person Identity via Large-Scale Observations. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_44
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DOI: https://doi.org/10.1007/978-3-319-16634-6_44
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