人臉辨識是影像視覺中重要的研究領域，人臉辨識屬於非接觸性的機制，因此以人臉作為身分識別的安全監控技術已漸漸融入高科技的生活之中。然而光源是影響人臉辨識一個很重要的因素，因為同一張人臉在不同光源環境中會有很大的不同，因此在不同的光源環境之中白天或夜晚時能順利對不同照度之人臉影像，作出正確之判斷，是一個值得研究的課題。
通常影像是由於光源照射在物體上的反射光所形成，其影像像素強度的不同則顯現出不同的顏色，或者顯現出不同的灰階分佈，因此對光源照射呈現不均勻的影像，本論文擬利用基於區塊為基礎的局部對比強化的演算法，來解決照度不同及光源不均勻的問題，以構成一可作居家出入門禁之人臉辨識系統。
本論文所提出之可適應在不同照度下白天/夜晚之人臉辨識系統，首先對於輸入不同光照之影像，對人臉影像作局部對比強化(Local Contrast Enhancement)處理，主要是利用局部閥值(Local Threshold)保留低於閥值以下的重要灰階資訊，將高於閥值以上的灰階範圍給予刪剪，來作直方圖刪剪 (Histogram Clipping) 之處理，以得到人臉影像中重要的特徵資訊，然後再加上使用局部對比伸張強化的方法，得到一不受光照影響的人臉影像。以便在明暗不同的光照環境下，依然能夠清楚地擷取到人臉之特徵資訊。
在人臉特徵擷取的方法上是利用主要成份分析(Principal Component Analysis, PCA)法， PCA分析法主要是將原影像資料利用特徵值(eigenvalue)之計算，得到特徵向量，以便在特徵空間作辨識。最後將取得的影像特徵值使用歐氏距離的方法，與人臉影像資料庫的特徵參數互相比對，進行人臉之辨識。
此一可適應於白天/夜晚不同照度之人臉辨識系統，經實驗顯示，在不同光照強度下，對4個人臉影像，使用本論文所用之局部閥值經由直方圖刪剪處理，並且使用局部對比強化的方法，搭配主要成份分析(Principal Component Analysis，PCA) 特徵擷取的方法，證明能有效克服不同光源照度下之人臉辨識之困難，並提供有效之辨識率。

Human face recognition, which is a non-contact mechanism, is an important research area in image vision, and safety monitoring skill by human face identification is becoming a part of high technology living. However, light source is an important factor affecting human face recognition which differs according to the variation of light source. Thus, to correctly obtain human face images in different light environment, for example day or night, without affecting the recognition of the human face image is a valuable subject.
Usually images are formed by light reflected by objects. Different images pixels amplitude show different colors or different gray scale distribution, thus non-uniform images are presented. This paper is to use block based local contrast algorithm[9] to solve the problem of non-uniform light source, therefore a human face recognition system can be built as used in a house door monitor system.
In human face recognition system proposed in this paper, firstly, for images under different light sources, human face images are processed by Local Contrast Enhancement. Local threshold method is used to reserve important gray scales information lower than threshold, and those gray scales higher than threshold are clipped by Histogram Clipping to acquire important feature information of human face images. Then local contrast extension and enhancement method is used to obtain human face images immune to light sources variation. As a result, in different light source environments, human face feature information can still be clearly obtained.
Methods of fetching features of human face make use of Principal Component Analysis (PCA), The PCA method is mainly to get the eigenvalues and eigenvector. PCA dimensions of original image data are reduced and those image data are recognized in eigen space. Finally, these obtained eigenvalues are compared with eigen parameters stored in human images database by Euclid method to recognize human faces.
The human face recognition system adaptive to day or night, through experiments of four human face images under different luminance, by local threshold method, Histogram Clipping method, local contrast enhancement method, and Principal Component Analysis features for recognition, It been proven effectively improves the difficult problems of human face recognition in different luminance environment. And has good recognition rate.

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