隨著資訊科技之演進，以臉部特徵來辨識性別之人機互動相關應用，逐漸變成一個相當受到重視的議題。本論文提出一個以臉部特徵來做性別辨識之系統，首先要能偵測取得之影像是否有臉部存在，故對取得之影像利用物件偵測演算法對影像進行人臉部位偵測並定位，接著將切割出的臉部影像做正規化。在特徵擷取方面，我們考慮三種特徵分析方式來選用特徵值，分別為辨別能力分析(Discrimination Power Analysis, DPA)、二維主成分分析(2-D principal component analysis, 2DPCA)及興趣運算元(Interest Operator, IO)。並針對選出之每一種特徵分別測試其成效，同時也將三種特徵結合來測試，最後利用多數決之投票方式來決定性別。在台灣近視人口的比例相當高，根據研究，人臉影像中眼鏡的存在對特徵擷取會有不良影響，本研究針對此問題，提出了眼鏡偵測與消除方法以減少因眼鏡存在，造成對相關特徵之影響。由實驗結果可以得知本研究提出的性別辨識方法具有不錯的效能。

Gender recognition based on facial features is an attractive issue in the recent years. In this thesis, a gender recognition system was proposed. For an input image, the facial region is firstly segmented by face detection algorithm. The segmented images are normalized to a uniform size. Three methods, Discrimination Power Analysis (DPA), 2-D Principal Component Analysis (2DPCA) and Interest Operator (IO) are considered to extract the features of the normalized face image. The DPA is used to select the coefficients from the DCT transformed image of the face image which provide more discriminating capability. The 2DPCA is based on the image matrix, and it is simpler to use for image feature extraction. The IO calculates variation information in different directions of the image pixel intensity. In order to evaluate the affect of the recognition rate for these images with eye glasses, the proposed method considered two methods to erase the eye glasses and their results are compared. To compare the effectiveness of the features extracted from the three ways, different combination ways for these features are considered and the results are compared. Finally, the majority voting method is applied to get the results for the features extracted from all three methods. From the experimental results, it is shown that the proposed method can perform well.

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