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The CMU-Pittsburgh action units-Coded Facial Expression Image Database provides a valuable test-bed with which multiple approaches to facial expression analysis may be tested

Comprehensive Database for Facial Expression Analysis

FG, pp.46-46, (2000)

Cited by: 2897|Views191
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Abstract

Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, tr...More

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Introduction
  • Significant effort has occurred in developing methods of facial feature tracking and analysis.
  • Analysis includes both measurement of facial motion and recognition of expression.
  • Because most investigators have used relatively limited data sets, the generalizability of different approaches to facial expression analysis remains unknown.
  • In the absence of comparative tests on common data, the relative strengths and weaknesses of different approaches is difficult to determine.
  • A large, representative test-bed is needed with which to evaluate different approaches
Highlights
  • Significant effort has occurred in developing methods of facial feature tracking and analysis
  • We describe the characteristics of databases that map onto this problem space, and evaluate Phase 1 of the CMU-Pittsburgh action units-Coded Facial Expression Database against these criteria
  • Development of robust methods of facial expression analysis requires access to databases that adequately sample from this problem space
  • The CMU-Pittsburgh action units-Coded Facial Expression Image Database provides a valuable test-bed with which multiple approaches to facial expression analysis may be tested
Conclusion
  • The problem space for facial expression analysis includes multiple dimensions.
  • These include level of description, transitions among expressions, distinctions between deliberate and spontaneous expressions, reliability and validity of training and test data, individual differences among subjects in facial features and related characteristics, head orientation and scene complexity, image characteristics, and relation to other non-verbal behavior.
  • Development of robust methods of facial expression analysis requires access to databases that adequately sample from this problem space.
  • In current and new work, the authors will further increase the generalizability of this database
Tables
  • Table1: FACS Action Units
  • Table2: Miscellaneous Actions
  • Table3: CMU-Pittsburgh AU-Coded Facial
Download tables as Excel
Funding
  • This research was supported by grant number R01 MH51435 from the National Institute of Mental Health
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