Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 31, Issue 11, Pages 2106-2111Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2009.42
Keywords
Face and gesture recognition; machine learning; computer vision
Funding
- US National Science Foundation (NSF) [0808767]
- UC [10202]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0808767] Funding Source: National Science Foundation
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Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.
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