在這篇論文中，我們使用貝氏估計的架構去針對人臉區塊相似度及由訓練資料所累積的分布來達到對人臉照片做性別辨識的目的。我們的人臉性別辨識主要包含三個程序。在前處理的過程中，我們先對face dataset 使用AAM做位置上的fitting，隨後將fitting完成所對應到的landmark point以30x30 pixels 做patch extraction，並將所對應的patch按順序以對應的路徑做資料存取。我們相信每個AAM抓下來所對應的patch有各自不同的樣本特徵，我們使用Local Binary Patch當作feature去對每一個patch做labels，並且也按順序存在對應的路徑裡。最後我們認為圖像資料庫的組成對於由訓練資料所建立的男性及女性累積分布有密切的關係。我們提出一個以特徵臉與K均值分群法的結合方式，從我們自行收集並分類的男性及女性照片資料庫中選取出具有代表性的男性及女性照片，接著由剩餘的照片視為訓練照片並將其特性根據不同圖像資料庫的數量及組成建立出對應的男性及女性累積分布。我們共訓練了27個獨立的貝氏分類器，每個分類器有不同的分類結果，在實驗結果(a)(b)為我們將訓練的圖片拿去做測試，以檢查訓練的結果具有一定的辨識能力，在實驗結果(c)(d)我們使用其他非訓練的圖片做測試，測試我們的性別分類器的分類能力。
在各自測試完27個分類器的結果後，我們再使用patch weighting的方法，計算出27個patch整體的男女分類率，而不是只有單一個patch的分類率，實驗顯示我們的分類率可以比沒有做weighting的效果更好5%左右。

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