Frontalization and Adaptive Exponentially Weighted Average Ensemble Rule for Deep Learning Based Facial Expression Recognition
| Autor: | Kai-Yuan Tsai, 蔡開遠 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Nowadays, Automatic Facial Expression Recognition (FER) is an important technique in human-computer interfaces and surveillance systems, has attracted significant attention in pattern recognition and computer vision. Automatic systems for facial expression recognition receive the input (a static facial image or a facial image sequence) and classify it into one of the basic expressions (anger, sad, surprise, happy, disgust and fear, neutral and so on). Our work will focus on methods based on facial static images and it will consider the seven basic expressions. In this paper, we proposed a CNN based system with face frontalization and Hierarchical architecture for FER. The frontalized algorithm can align the small angle rotation (in-of-plane or out-of-plane) and use the face detection to remove the background noise, the adaptive exponentially weighted average ensemble rule can search the optimal weight according to the efficiency of classifier to improve the robust FER system. As a result, we perform the proposed system on some popular databases, the simulation results show that it is very effective for facial expression recognition, we achieve an accuracy rate surpassing the state-of-the-art system. Keyword: facial expression; convolutional neural networks; computer vision; face frontalization; hierarchical structure. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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