The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Unsupervised Joint Alignment of Complex Images
Open accessAbstract
Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm.
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Cites
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Self-citationData driven image models through continuous joint alignment
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DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Effective face frontalization in unconstrained images
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Past, Present, and Future of Face Recognition: A Review
Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020) · Electronics · doi:10.3390/electronics9081188 · Cited by 560
Similarity Scores Based on Background Samples
Wolf, L., Hassner, T., & Taigman, Y. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-12304-7_9 · Cited by 279
Genimage: A million-scale benchmark for detecting ai-generated image
Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tu, Z., Hu, H., Hu, J., & Wang, Y. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-3398 · Cited by 128
Metadata
- Authors
- Huang, G. B., Jain, V., & Learned-Miller, E.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2158844819
- Cited by
- 339
- References
- 16
- FWCI
- 10.13
- Type
- conference paper
- Language
- en
- Open access
- green
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