Deep learning for universal emotion recognition in still images
This work propose a methodology for still image facial expression. The proposed method contains a face detection and alignment module followed by a deep convolutional neural network (CNN) that outputs a seven emotions probability vector.
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/119073 |
| Acceso en línea: | https://hdl.handle.net/2117/119073 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Machine learning Human face recognition (Computer science) facial expression recognition convolutional neural networks overfitting face analysis dataset Xarxes neuronals (Informàtica) Aprenentatge automàtic Reconeixement facial (Informàtica) Àrees temàtiques de la UPC::Informàtica |
| Sumario: | This work propose a methodology for still image facial expression. The proposed method contains a face detection and alignment module followed by a deep convolutional neural network (CNN) that outputs a seven emotions probability vector. |
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