Extraction of Useful Features from Neural Network for Facial Expression Recognition
| Autor: | Teruhisa Hochin, Hiroki Nomiya, Naoki Imamura |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Facial expression
Artificial neural network Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Pattern recognition 02 engineering and technology Lifelog Correlation stomatognathic diseases ComputingMethodologies_PATTERNRECOGNITION Facial expression recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | SNPD |
| DOI: | 10.1109/snpd.2019.8935652 |
| Popis: | Facial expression intensity has been proposed to digitize the degree of facial expressions in order to retrieve impressive scenes from lifelog videos. However, correlation of facial features compared to each facial expression is not determined objectively. Therefore, we recognize facial expressions by using a neural network and calculate the contribution score of input toward output. We verify the score correctly by comparing the accuracy transitions depending on reducing useful and useless features, and process the score statistically. As a result, we extract useful facial features from the neural network. |
| Databáze: | OpenAIRE |
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