Facial Expression Recognition Using Deep Siamese Neural Networks with a Supervised Loss function
| Autor: | Mohammad H. Mahoor, Wassan Hayale, Pooran Negi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning 010401 analytical chemistry Pattern recognition Function (mathematics) 010501 environmental sciences 01 natural sciences Signal Class (biology) 0104 chemical sciences Identification (information) Facial expression recognition Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | FG |
| DOI: | 10.1109/fg.2019.8756571 |
| Popis: | This paper presents a novel algorithm for an end-to-end facial expression recognition(FER) system based on deep Siamese neural networks equipped with a supervised loss function. Our method learns a powerful FER system by dynamically modulating verification signal over identification/classification signal. The identification signal increases the inter-class variations by maximizing the distance between the features for different classes, while the verification signal reduces the intra-class variations by minimizing the distance between features for the same class. We have evaluated our method on the AffectNet dataset [10] and achieved promising results compared to other deep learning models. |
| Databáze: | OpenAIRE |
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