The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cost-Sensitive Face Recognition
Abstract
Most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. In this paper, we argue that this is far from a reasonable setting because, in almost all application scenarios of face recognition, different kinds of mistakes will lead to different losses. For example, it would be troublesome if a door locker based on a face recognition system misclassified a family member as a stranger such that she/he was not allowed to enter the house, but it would be a much more serious disaster if a stranger was misclassified as a family member and allowed to enter the house. We propose a framework which formulates the face recognition problem as a multiclass cost-sensitive learning task, and develop two theoretically sound methods for this task. Experimental results demonstrate the effectiveness and efficiency of the proposed methods.
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Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
Ordinal hyperplanes ranker with cost sensitivities for age estimation
Chang, K.-Y., Chen, C.-S., & Hung, Y.-P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995437 · Cited by 342
Tong, T., Ledig, C., Guerrero, R., Schuh, A., Koikkalainen, J., Tolonen, A., Rhodius, H., Barkhof, F., Tijms, B., Lemstra, A. W., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.06.012 · Cited by 54
Metadata
- Authors
- Zhang, Y., & Zhou, Z.-H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-10
- Volume
- 32
- Issue
- 10
- Pages
- 1758-1769
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2214725774
- Cited by
- 162
- References
- 1
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, Y., & Zhou, Z.-H. (2010). Cost-Sensitive Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2009.195
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