Frontalization with Adaptive Exponentially-Weighted Average Ensemble Rule for Deep Learning Based Facial Expression Recognition
| Autor: | Kai-Yuan Tsai, Jian-Jiun Ding, Yih-Cherng Lee |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Pattern recognition 02 engineering and technology Facial recognition system Convolutional neural network Expression (mathematics) ComputingMethodologies_PATTERNRECOGNITION Face (geometry) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | APCCAS |
| DOI: | 10.1109/apccas.2018.8605689 |
| Popis: | Automatic Facial Expression Recognition (FER) is an important technique in human-computer interfaces and surveillance systems. It classifies the input facial images into one of the basic expressions (e.g., anger, sad, surprise, happy, disgust, fear, and neutral) and has attracted significant attention in pattern recognition and computer vision. In this paper, we proposed an advanced convolutional neural networks based FER system. It applies the techniques of face frontalization and feature positioning to reduce the effects of background noise and non-prominent parts. Moreover, the hierarchical structure together with the adaptive exponentially weighted average ensemble are adopted to further improve the accuracies. Simulations on several datasets show that the proposed system outperform state-of-the-art FER methods and can well identify the expression of a person. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|