研究生: |
鄭亦曾 Yi-Tseng Cheng |
---|---|
論文名稱: |
結合局部區域與仿生特徵之年齡估測 Landmark Oriented Generalized Biologically Inspired Features for Age Estimation |
指導教授: |
徐繼聖
Gee-Sern Hsu |
口試委員: |
賴尚宏
Shang-Hong Lai 王鈺強 Yu-Chiang Wang 郭景明 Jing-Ming Guo 亞魯 ArulMurugan Ambikapathi |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 51 |
中文關鍵詞: | 年齡估測 、仿生特徵 |
外文關鍵詞: | Age estimation, Bio-Inspired feature |
相關次數: | 點閱:197 下載:3 |
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本論文提出了Generalized Biologically Inspired Features (GBIFs),和移動式分類視窗來做年齡估測。GBIF比BIF還要更有彈性,可以適應更多不同種類的影像擷取出臉部年齡相關特徵,BIF特徵被廣泛用於年齡估測特徵,但沒有論文去研究此特徵,本論文提出的GBIF除了可以在臉部局部區域可以做選擇,頻帯(Band)數量、相鄰取極值濾波後影像數目 和濾波器方向 都可以做調整;在分類的部分,先區分年齡群組再進行估測可降低錯誤率,這種分類法稱為分層式年齡估測(Hierarchical Age Estimation),許多文獻都是使用此方法,但不同的文獻就有不同的年齡群組與不同的群組分界線,本論文提出移動式分類視窗,以定義出最佳的年齡群組分界線,同時討論了不同分層式分類架構,決定出最好的分層式分類架構,再搭配軟邊界回歸用於指標性的公開年齡資料庫,如FG-NET與MORPH作測試,實驗證明效能近似甚或優於近期文獻中所提之年齡估測方法。
We propose the Generalized Biologically Inspired Features (GBIFs) and a moving segmentation scheme followed by soft boundary regression for age estimation. The GBIF is more advantageous than the Bio-Inspired Feature (BIF) for capturing age-related facial traits. The moving segmentation is proposed to better determine the age groups, leading to an improvement on the age estimation accuracy. Different from most approaches that segment the age groups in an ad-hoc way, the moving segmentation allows one to define age groups using the local minima in the misclassification rate across ages. The extraction of the GBIF depends on the partition of component regions defined by facial landmarks. In addition to the partition of component regions, we also study the appropriate age grouping and hierarchical classification, and determine the best configuration for age estimation. The proposed approach with the most appropriate settings outperforms most of the state of the art on two benchmarks, FG-NET and MORPH.
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