Abstract
This paper studies the problem of automatic person enrollment based on face-gait fusion within a context of anonymous identification. Enrollment should determine whether an observed subject has been seen before or not. Traditionally, this process has been embedded into a major identification system and its potential has been undervalued. This work claims that enrollment can be considered as a task in itself, and that there are real applications that can benefit from exclusively managing it. To this end, it is shown that the enrollment error model is different from that of anonymous identification. Enrollment experiments, conducted over three types of random permutations of probe samples, showed the benefits of face-gait fusion over single biometrics.
This work has been supported by the grants P1-1B2012-22 and PREDOC/2012/05 from Universitat Jaume I, PROMETEOII/2014/062 from Generalitat Valenciana, and TIN2013-46522-P from the Spanish Ministerio de Economía y Competitividad.
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Notes
- 1.
Note that the converse holds when scores encode similarities (rather than distances).
- 2.
The reader will find more details on methods and datasets in next sections.
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Ortells, J., Mollineda, R.A. (2015). Person Enrollment by Face-Gait Fusion. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_54
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