Title | ||
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Cascaded Multi-level Transformed Dirichlet Process for Multi-pose Facial Expression Recognition. |
Abstract | ||
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As an essential way of human emotional behavior understanding, facial expression recognition (FER) has been studied extensively in recent years. However, the existing methods of FER are typically based on near-frontal face data. High-recognition accuracy for multi-pose FER continues to be a challenge. In this paper, we present a novel cascaded multi-level Transformed Dirichlet Process (cml-TDP) model for multi-pose FER. The top-level structure of the cml-TDP model has been carefully designed to make coarse-to-fine prediction, and the outputs of the model are fused for robust and accurate estimation at each level. There are three primary merits to cml-TDP. First, pose is explicitly introduced into cml-TDP so that separate training and parameter tuning for each pose is not required. Second, cml-TDP describes an image by its detected positions and appearance features to implicitly construct geometric constraints. Third, cml-TDP can learn an intermediate facial expression representation subject to geometric constraints. By sharing the pool of spatially coherent features over expressions and poses, we provide a scalable solution for multi-pose FER. The proposed model has been evaluated on two benchmark databases, BU-3DFE and RAFD, and achieved 79.33% and 75.00% FER accuracy on these two datasets, respectively, which has outperformed current state-of-the-art FER methods. |
Year | DOI | Venue |
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2018 | 10.1093/comjnl/bxy016 | COMPUTER JOURNAL |
Keywords | Field | DocType |
facial expression recognition,multi-level Transformed Dirichlet Process,appearance features,geometric constraints,spatially coherent features | Dirichlet process,Pattern recognition,Facial expression recognition,Computer science,Theoretical computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
61 | 11 | 0010-4620 |
Citations | PageRank | References |
3 | 0.40 | 15 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Qirong Mao | 1 | 261 | 34.29 |
Feifei Zhang | 2 | 61 | 19.93 |
Liangjun Wang | 3 | 7 | 1.79 |
Sidian Luo | 4 | 3 | 0.73 |
Ming Dong | 5 | 105 | 13.70 |