Facial Attribute Recognition by Recurrent Learning With Visual Fixation
| Autor: | Jaeyeon Lee, Hyunjoong Cho, Seungjoon Yang, Jaehong Kim, Jinhyeok Jang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies Facial expression Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Pattern recognition 02 engineering and technology Facial recognition system Computer Science Applications Visualization Human-Computer Interaction stomatognathic diseases ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Software Information Systems |
| Zdroj: | IEEE transactions on cybernetics. 49(2) |
| ISSN: | 2168-2275 |
| Popis: | This paper presents a recurrent learning-based facial attribute recognition method that mimics human observers’ visual fixation. The concentrated views of a human observer while focusing and exploring parts of a facial image over time are generated and fed into a recurrent network. The network makes a decision concerning facial attributes based on the features gleaned from the observer’s visual fixations. Experiments on facial expression, gender, and age datasets show that applying visual fixation to recurrent networks improves recognition rates significantly. The proposed method not only outperforms state-of-the-art recognition methods based on static facial features, but also those based on dynamic facial features. |
| Databáze: | OpenAIRE |
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