本論文的研究目的為特定人物之辨識，有關人臉辨識之相關方法有：小波臉（Waveletface）、特徵臉（Eigenface）、線性鑑別分析（LDA）等方法，在本論文所採用的方法為LDA及改良式LDA，若類別內散佈矩陣（Within-class scatter matrix）為非奇異矩陣時，則使用LDA轉換，但類別內散佈矩陣為一奇異矩陣時，則採用改良式LDA轉換，再配合使用最小距離分類器來進行人臉辨識。但此方式一定會在人臉資料庫裡選一個與受測人臉影像最相近的人物來表示該受測人臉之身分，因此此方式無法將非資料庫裡的人物界定出來。為了使本系統能夠界定出非特定人物之人臉，本論文建立的幾個陌生人的資料庫，讓特定人物之人臉影像與陌生人之人臉影像一同進行LDA轉換，藉此使本系統具備有辨識陌生人的能力。

This research studies techniques of recognizing facial images of specific persons. Many methods have been used to recognize specific facial images, which include Waveletface method, Eigenface method, LDA method, etc. In this research work, LDA and modified LDA methods are used as primary tools for recognizing the facial images of a specific person from digital images obtained by a CCD camera. LDA method handles images containing a non-singular within-class scatter matrix, while the modified LDA method tackles image with a singular matrix. Since LDA methods serve mainly to locate the person with the closest facial image to the one in the input image, from a number of people in the data bank, a total stranger will also be interpreted as one of the people in the bank. A novel approach is proposed in this work to include a data bank comprising a large number of strangers’ faces. A number of face images from the bank are then used to form a specific stranger. The LDA or modified LDA methods can then be used to effectively interpret a face image as the pre-determined master or a non-master/stranger.

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