CNN expression recognition based on feature graph
| Autor: | Jun-Li Zhao, Lin-Lin Xu, Fu-Xing Wang, Shu-Mei Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Facial expression
Artificial neural network Computer science business.industry 010401 analytical chemistry Feature extraction Decision tree Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Facial expression recognition Graph (abstract data type) AdaBoost Artificial intelligence 0210 nano-technology business Cascading classifiers |
| Zdroj: | 2017 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac.2017.8243428 |
| Popis: | To solve the problem of training rate decline in neural network caused by too much noise in the traditional image, a new method of expression recognition based on CNN was proposed. First, in order to narrow the face range, face image could be detected from the original image by using the AdaBoost cascade classifier. Then, the coordinates of the eye, mouth and other key parts and brow, nasolabial and other fine features could be marked by using the Harr feature and the regression tree collection algorithm. After the fusion, the feature points were generated and sent into the neural network for training. This method was tested on the 2940 face expression peak images selected from the CK + data set. Comparing to the original picture training plan, the method increased the rate by about one in a tenth. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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