Unsupervised Domain Adaptation with Generative Adversarial Networks for Facial Emotion Recognition
| Autor: | Yingruo Fan, Victor O. K. Li, Jacqueline C.K. Lam |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Facial expression
Domain adaptation Computer science business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Adversarial system ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Emotion recognition Transfer of learning business computer Natural language processing Generative grammar 0105 earth and related environmental sciences |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622514 |
| Popis: | Cross-dataset facial emotion recognition (FER) aims to reduce the discrepancy between the source and the target facial database. The topic is very challenging in FER, where facial features differ across different domains, such as ethnicity, age, gender and environmental condition. In practice, the labels of target facial expression database may be unavailable, making it impossible to fine-tune a pre-trained model via supervised transfer learning. To address this issue, we propose an unsupervised domain adaptation framework with adversarial learning for cross-dataset FER. We perform cross-dataset FER on three well-known publicly available facial expression databases, viz. CK+, Oulu-CASIA, and RAF-DB, showcasing the efficiency of our proposed approach. |
| Databáze: | OpenAIRE |
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