Gender Recognition from Human Faces by Using a Shared-Integral-Image Approach
學年 97
學期 2
出版（發表）日期 2009-05-01
作品名稱 Gender Recognition from Human Faces by Using a Shared-Integral-Image Approach
作品名稱（其他語言）
著者 Shen, Bao-cheng; Hsu, Hui-huang; Chen, Chu-song
單位 淡江大學資訊工程學系
出版者 Allahabad: Pushpa Publishing House
著錄名稱、卷期、頁數 Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.101-112
摘要 We develop an approach for gender recognition based on human faces. We combine rectangle features extracted from the human-face region into a rectangle-feature vector (RFV). The RFV is computationally fast and effective in encoding intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present an effective gender identification approach. We then use nonlinear support vector machines for classification, and obtain more accurate recognition results. Experimental results show that our approach performs well for the Feret database.
關鍵字 Gender Recognition; AdaBoost; Real Ad-aBoost; Support Vector Machine; Integral Image
語言 en
ISSN 0974-3261
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度
國別 IND
公開徵稿
出版型式 紙本
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