Local Binary Patterns based Hierarchical Method for Facial Expression Recognition
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === So far, automatic human facial expression recognition has been a challenging problem in computer vision area, and brings strong impacts in important applications in many areas such as human-computer interaction and data-driven animation. In this thesis, I use t...
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ndltd-TW-100NTU053920092016-04-04T04:17:30Z http://ndltd.ncl.edu.tw/handle/92797046499255942918 Local Binary Patterns based Hierarchical Method for Facial Expression Recognition 以區域二元圖樣為基礎之階層式人臉表情辨識 Hsing-Han Ho 何星翰 碩士 國立臺灣大學 資訊工程學研究所 100 So far, automatic human facial expression recognition has been a challenging problem in computer vision area, and brings strong impacts in important applications in many areas such as human-computer interaction and data-driven animation. In this thesis, I use the Local Binary Patterns method, which was used commonly in face recognition, to recognize the facial expressions. Furthermore, I propose a novel idea to improve the result. This new method is called “Hierarchical Facial Expression Recognition”. In traditional facial expression recognition, the researchers use training data to make a classifier, and classify the test expression data into one of the several basic expressions directly. Hierarchical method separates the recognition process into few stages, each stage using specific methods to deal with the specific expression to step by step recognize the target image. The overall recognition rates of my system on two expression databases are about 80% and 86% with pure Local Binary Patterns algorithm, and reach 87% and 89% after using the hierarchical method. This work shows that the Local Binary Patterns method is practical in facial expression recognition, and the idea of hierarchical facial expression recognition can further improve the result. Ming Ouhyoung 歐陽明 2012 學位論文 ; thesis 46 en_US |
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碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === So far, automatic human facial expression recognition has been a challenging problem in computer vision area, and brings strong impacts in important applications in many areas such as human-computer interaction and data-driven animation. In this thesis, I use the Local Binary Patterns method, which was used commonly in face recognition, to recognize the facial expressions. Furthermore, I propose a novel idea to improve the result. This new method is called “Hierarchical Facial Expression Recognition”. In traditional facial expression recognition, the researchers use training data to make a classifier, and classify the test expression data into one of the several basic expressions directly. Hierarchical method separates the recognition process into few stages, each stage using specific methods to deal with the specific expression to step by step recognize the target image. The overall recognition rates of my system on two expression databases are about 80% and 86% with pure Local Binary Patterns algorithm, and reach 87% and 89% after using the hierarchical method. This work shows that the Local Binary Patterns method is practical in facial expression recognition, and the idea of hierarchical facial expression recognition can further improve the result.
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author2 |
Ming Ouhyoung |
author_facet |
Ming Ouhyoung Hsing-Han Ho 何星翰 |
author |
Hsing-Han Ho 何星翰 |
spellingShingle |
Hsing-Han Ho 何星翰 Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
author_sort |
Hsing-Han Ho |
title |
Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
title_short |
Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
title_full |
Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
title_fullStr |
Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
title_full_unstemmed |
Local Binary Patterns based Hierarchical Method for Facial Expression Recognition |
title_sort |
local binary patterns based hierarchical method for facial expression recognition |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/92797046499255942918 |
work_keys_str_mv |
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