Abstract
In the previous chapter, the concept of separating data into an evaluation and development dataset for optimization and testing was discussed. In Chaps. 4 through 6 we saw the role a development dataset can play in optimizing biometric system components for best recognition performance. Yet, optimizing a biometric system to a development dataset may expose it to inherent data bias , and development results may lead to the selection of biometric systems that perform poorly in practice. To be certain, a particular biometric system configuration is suitable for practical use it should be put through an evaluation process, whereby it is tested against previously unseen data. As part of our demonstrative GRF-based gait recognition experiment, this chapter puts the best biometric configurations discovered in earlier chapters to the test by applying them to the previously unseen samples from an evaluation dataset.
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Reference
Cattin, Philippe C. 2002. Biometric authentication system using human gait. Ph.D. Thesis, Swiss Federal Institute of Technology, Zurich, Switzerland.
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© 2016 Springer International Publishing Switzerland
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Mason, J.E., Traoré, I., Woungang, I. (2016). Measured Performance. In: Machine Learning Techniques for Gait Biometric Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-29088-1_8
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DOI: https://doi.org/10.1007/978-3-319-29088-1_8
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Online ISBN: 978-3-319-29088-1
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