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標題: 用於未校準人臉影像之基於多模式資訊融合的性別辨識系統
Gender recognition based on multi-model information fusion for unaligned facial images
作者: 張佑瑞
Chang, Yu-Jui
關鍵字: Gender classification;性別分類;Face rectification;Information fusion;人臉校正;資訊融合
出版社: 電機工程學系所
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於本論文,我們提出一個基於多模式資訊融合的性別辨識系統。而所提出的性別辨識系統主要由四個部分所構成:人臉偵測與校正、人眼偵測、特徵擷取和性別分類器。為了驗證所提出系統的效能,我們使用low cost webcam拍攝大量不同大小人臉的實驗影像。實驗結果顯示我們提出的方法除了可以正確地找出人臉並且能準確找到人眼位置。此外,所提出的系統性別辨識率可達到95%。這些結果驗證我們所提出的方法除了人臉偵測之外,也可以達到性別辨識的效果。

In the thesis, we proposed a gender recognition scheme based on multi-model information fusion. The proposed gender recognition scheme is composed of four parts: face detection and rectification, eye detection, feature extraction, and gender classifier. To evaluate the proposed scheme, a large number of images containing different-size faces are captured by using low-cost webcam. Experimental results show that our proposed scheme can detect facial regions as well as eyes well. In addition, the accuracy of gender recognition for our scheme is more than 95%. These results demonstrate that our proposed scheme can achieve not only face detection but also gender recognition.
其他識別: U0005-2907201114521700
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