光線變異會破壞人臉影像的品質，並降低人臉辨識率。而本論文提出一個可以降低光線干擾影像的演算法。首先改良了Gamma校正，使得當中的係數γ會隨著圖像的亮度直方圖是否正常而做調整，進而解決了輪廓在陰暗處不明顯的問題，此稱為極值局部顏色校正。接著調整傳統的白平衡，特別將原本紅綠藍三通道亮度平均調整為最接近圖像亮度舒適區的兩通道平均，使得白平衡不會再發生過度曝光及顏色飽和度流失的問題，將此稱為柔和化白平衡。最後提出了三項客觀的圖像一致性衡量指標來為演算法做評比，此舉解決了圖像品質改良以往要做問卷耗人工的評比方式。本文以圖像平均亮度，個人圖像亮度變動及個人膚色比例變動來作為演算法是否對不同環境有亮度一至性及膚色一至性來評比。本文以CMU-PIE人臉資料庫為驗證樣本，來與實驗室之前的作品做比較。結果顯示由人眼就圖片品質改善有很大的提升，且以本文的圖像衡量指標在相同類別來判斷圖像一致性，其客觀度媲美做問卷的評比方式。最後將結果圖與梯度臉做結合，而後搭配特徵臉及最近鄰居演算法進行分類。其分類結果在只取前四根特徵臉的情況下，辨識率就領先15個百分點。顯示本文的方法，不但能滿足人眼，套用到機器視覺作辨識，成效也卓越。 Extracting facial feature is a key step in face recognition (FR). Inaccurate feature extraction very often results in erroneous categorizing of persons. Especially in extremely condition, illumination variation is a crucial issue in FR and images which are not properly corrected can look either bleached out or too dark and eventually introduces a false acceptance. In this thesis, we present a novel framework, local color correction in extremely illumination condition (LCC-EIC), working toward to reproduce facial colors accurately. Adaptively varying the amount of gamma correction based on the histogram of the image changes on the brightness, and thus solving the problem in which the contour disappeared from the face under the shadowed side. To remove unrealistic color casts, so that faces which appear white in person are rendered white in our images. Smoothing white balance (SWB) is followed up. The original mean of RGB three color channels to the mean of any two channels which nearest the comfort intensity zone is adaptively tuned up making the white balance would not have overexposed and loss of color saturation. Finally, we propose three objective indexes for image coherence to measure the performance of the proposed algorithm. The indexes are the average intensity of the image, the mean of the standard deviation on individual intensity, and the mean of the standard deviation of the ratio of RGB three color channels. Using these three indexes to evaluate the invariance of intensity and skin color under different lighting conditions can be regarded as almost equal to make a questionnaire for people. We use CMU-PIE face database and compare with our previous works. The result shows that the proposed algorithm makes the great improvement on image quality based on human eyes and our three image coherence indexes. In final, we combine our result with the related work of Gradientfaces for facial feature extraction, then using Eigenface and the nearest neighbor for FR. Experimental results show that this work leads 15% recognition rate compared to the related works with first four Eigenfaces. It means that our method not only satisfies the human vision, but also makes the better job on machine vision.