Cited by 373Face recognition and analysisFace and Expression RecognitionFace Recognition and Perception

Multi-PIE

Gross, Ralph, Matthews, Iain, Cohn, Jeffrey, Kanade, Takeo, Baker, Simon·2008·2008 8th IEEE International Conference on Automatic Face & Gesture Recognition

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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Registry

Crossref · 10.1109/afgr.2008.4813399

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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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