人臉辨識的結果討論

接下來針對人臉辨識的部分做討論，在表4.6 列出了照明、角度與遮擋三種因素組

合下的可能情形。情形(a)指的是在光線足夠環境且人臉上沒有陰影，人臉角度為正面對

表4.6 人臉辨識結果分析。

人臉特徵情形 辨識正確 辨識錯誤 辨識率

(a) 正面人臉 134 2 98.53%

(b) 有照明影響 88 5 94.62%

(c) 有角度影響 71 6 92.21%

(d) 有遮擋影響 50 11 81.97%

(e) 情形(b)及(c) 32 14 69.57%

(f) 情形(c)及(d) 8 25 24.24%

(g) 情形(b)及(d) 18 19 48.65%

(h) 情形(b)及(c)及(d) 1 6 14.29%

合計 376 26 93.53%

(a)

(b)

圖4.6 人臉辨識的錯誤結果，(a)人臉偵測後的結果，(b)人臉辨 識的結果，由左至右取出資料庫的人臉做展示。

Chapter 5     

第五章

結論與未來發展

本論文提出了一個可在一般數位相片中執行人臉偵測與人臉辨識的方法。由於數位 相片所拍攝的條件相當複雜，會造成偵測人臉及辨識的困難度。因此在人臉偵測的部 分，先使用膚色偵測來找出可能是人臉的區域，提升了偵測人臉的效率。然後使用賈伯 小波轉換取得人臉整體特徵，並利用區域保留投影將數量龐大的賈伯人臉特徵降至很低 的維度，有效增進了類神經網路的效能，使得偵測人臉的所在位置與人臉大小變得更為 準確。在人臉辨識的方面，則透過對資料庫中的人臉訓練類神經網路來辨識，在測試相 片輸入時，就能夠對於所選定的人臉做辨識，藉此辨識結果就可以將資料庫中含有相同 人物的相片挑選出來，達到基於人物身份的影像檢索的目的。

在未來的發展上分為人臉偵測與人臉辨識這兩部分。在人臉偵測的部分，由於偵測 是依靠人臉的特徵來進行，當因為人臉角度、照明環境與遮擋導致人臉特徵不明顯時，

就會使得偵測率明顯的下降。因此在後續的研究中，首要目標應該是研究改良人臉特徵 抽取的方法，避免因為角度與照明導致偵測率下降，同時也能應用在人臉辨識；然後再 搭配其他不需要依靠人臉特徵的方法，將人臉成功並準確的偵測出來。在人臉辨識的方 面，對於訓練樣本的數量及是否具有代表性的問題，是會直接影響到人臉辨識的辨識 率，要如何能從很少的訓練樣本中挑選出具有代表性的樣本來做訓練，將會是後續研究 中的首要目標。而從目前的人臉資料庫與本論文所使用的數位相片中來看，可以發現人

臉的樣本都是在相近的時間點所拍攝所得到的。但是人臉會因為隨著年齡增長使臉部特 徵產生變化，或著是因為臉部特徵的改變，如眼鏡、化妝等因素，可能會造成辨識的因 難，因此如何解決這種種問題，是值得繼續研究的。

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