The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class
Abstract
The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The Nature of Statistical Learning Theory
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Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778
Recognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745
Cited by
Learning a Similarity Metric Discriminatively, with Application to Face Verification
Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916
Compound facial expressions of emotion
Du, S., Tao, Y., & Martinez, A. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322355111 · Cited by 806
Self-citationThe CAS-PEAL large-scale chinese face database and baseline evaluations
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., & Zhao, D. (2008) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2007.909557 · Cited by 773
Pantic, M., & Patras, I. (2006) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2005.859075 · Cited by 580
Metadata
- Authors
- Martinez, A. M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-06
- Volume
- 24
- Issue
- 6
- Pages
- 748-763
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2107369107
- Cited by
- 813
- References
- 8
- FWCI
- 28.83
- Type
- journal article
- Language
- en
- Open access
- closed
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