研究生: |
曾郁凱 Tseng, Yu-Kai |
---|---|
論文名稱: |
基於區域二進位圖形直方圖之性別辨識使用Real AdaBoost Local Binary Pattern Histogram-Based Gender Classification Using Real AdaBoost |
指導教授: |
連震杰
Lien, Jenn-Jier |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2013 |
畢業學年度: | 101 |
語文別: | 英文 |
論文頁數: | 47 |
中文關鍵詞: | 性別辨識 、區域二進位圖形直方圖 、Real Adaboost |
外文關鍵詞: | Gender Classification, Local Binary Pattern Histogram, Real Adaboost |
相關次數: | 點閱:80 下載:0 |
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性別辨識是近年來熱門的研究課題,可應用於許多層面,如電子廣告、監視系統等等。本篇論文中我們開發出一套性別辨識即時系統,使用Local Binary Pattern Histogram 特徵以及Real AdaBoost演算法所訓練出的分類器進行性別判斷,此分類器可透過confidence value表示此次判斷的可信任度。本系統使用Shape Optimized Search進行眼角的自動偵測,並針對該方法與手動label 的眼角的位置之誤差提出統計的方法進行改善,並且為了有效地降低表情變化與臉部的些微晃動造成的雜訊影響,我們加入簡易人臉追蹤的機制並且對性別辨識的結果進行投票。經實驗證明,我們的系統可以有效的辨識男女且可應用於即時系統。
Gender classification is a hot research topic in recent years, which could be applied to many categories, e.g. electronic advertising, surveillance systems, etc. In this thesis, we present a gender classification system using local binary pattern histogram and Real AdaBoost learning method to create a strong classifier. The strong classifier outputs confidence value which presents the judgments with trust degrees. According to the error between manually labeled inner eye corner points and the eye corner points calculated by Shape Optimized Search algorithm, we present a statistical method to get the reference points which are close to the manually label inner eye corner points. In addition, in order to reduce the noise caused by facial expression changes and face’s small amplitude movements, the output of gender classification is determined by accumulating previous judgment results. The experimental results demonstrate that the system we purposed not only works effectively on single frame but could also applied in real-time systems.
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