Multi-tasking deep convolutional network architecture design for extracting nonverbal communicative information from a face
| Autor: | Heereen Shim, Kwang-Eun Ko, Inhoon Jang, Kwee-Bo Sim, Kyunghwan Cho |
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| Rok vydání: | 2018 |
| Předmět: |
Facial expression
Network architecture Computer science Cognitive Neuroscience Speech recognition 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Experimental and Cognitive Psychology 02 engineering and technology 050105 experimental psychology Nonverbal communication Artificial Intelligence ComputerApplications_MISCELLANEOUS Face (geometry) 0202 electrical engineering electronic engineering information engineering Human multitasking 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Facial region Regression problems Software |
| Zdroj: | Cognitive Systems Research. 52:658-667 |
| ISSN: | 1389-0417 |
| Popis: | Facial expressions convey not only emotions but also communicative information. Therefore, facial expressions should be analysed to understand communication. The objective of this study is to develop an automatic facial expression analysis system for extracting nonverbal communicative information. This study focuses on specific communicative information: emotions expressed through facial movements and the direction of the expressions. We propose a multi-tasking deep convolutional network (DCN) to classify facial expressions, detect the facial regions, and estimate face angles. We reformulate facial region detection and face angle estimation as regression problems and add task-specific output layers in the DCN’s architecture. Experimental results show that the proposed method performs all tasks accurately. In this study, we show the feasibility of the multi-tasking DCN for extracting nonverbal communicative information from a human face. |
| Databáze: | OpenAIRE |
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