The DOI matches its Crossref record; 13 of the 14 references it cites were found in a registry or an index.
Multi-PIE
Abstract
A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
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Cites
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Self-citationThe FERET database and evaluation procedure for face-recognition algorithms
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Self-citationActive Appearance Models Revisited
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Self-citationXM2VTSDB: The Extended M2VTS Database
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Independent comparative study of PCA, ICA, and LDA on the FERET data set
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1 of the 14 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- kanade, 1998, the 3d room: digitizing time-varying 3d events by synchronizing multiple video streams, Technical Report CMU-RI-TR-98-134
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A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
Self-citationSupervised Descent Method and Its Applications to Face Alignment
Xiong, X., & la Torre, F. D. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.75 · Cited by 1,926
PCANet: A Simple Deep Learning Baseline for Image Classification?
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2475625 · Cited by 1,635
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
Peng, Y., Ganesh, A., Wright, J. N., Xu, W., & Ma, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.282 · Cited by 825
Deformable Model Fitting by Regularized Landmark Mean-Shift
Saragih, J. M., Lucey, S., & Cohn, J. F. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0380-4 · Cited by 787
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Metadata
- Authors
- Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S.
- Affiliations
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- Microsoft (United States) (US)
- Venue
- 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 1-8
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2296659146
- Cited by
- 373
- References
- 14
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 11 · 2023: 13 · 2022: 20 · 2021: 29 · 2020: 22 · 2019: 26 · 2018: 20 · 2017: 17 · 2016: 31 · 2015: 25 · 2014: 30 · 2013: 27 · 2012: 28
- FWCI
- 14.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Funders
- Yale University
- Sandia National Laboratories
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- CMU-PIE, face recognition, face database, pose variation, illumination variation, facial expressions, PCA, LDA
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