摘 要：
人臉辨識是一個相當成熟、普遍的題目，若要達到較高的辨識率，仍需要克服許多問題。目前公認的主要問題包含了光照方向、表情變化等因素，另外，樣本數不足也會造成辨識率下降。本研究針對現實面的樣本取得問題，提出使用單一影像訓練樣本的方法，以提升人臉辨識率。
本研究提出了一個運用單一張影像做人臉辨識的方法，先使用影像處理方式產生多張虛擬影像以取得適合的訓練樣本數，並利用Retinex演算法降低光線的影響，然後根據人臉資料庫的特性來計算適合的特徵維度數，再使用最近特徵空間的分類方法保留訓練樣本的區域結構資訊，以得到較佳的辨識率。實驗結果顯示，本研究提出的辨識方法能達到七成以上的辨識效果。

英文摘要：
Face recognition has attracted much attention in recent years. In order to achieve the high recognition rates, many problems such as poses, illumination, face expression, the training samples, simple dimensions, etc. should be solved.
In this thesis, a face recognition algorithm using a single training sample is designed. Basically, the main idea is to increase the training samples from a single face sample. Multiple simulated images are first composed to generate a suitable training sample set. A Retinex algorithm is next used to reduce the impact of lighting. Second, a PCA subspace is found to efficiently represent the faces. The discriminant projection axes are next found using a nearest feature space(NFS) embedding method. The NFSE method embeds the distance metric of point to spaces into the discriminant analysis. Some experimental results are conducted to show the validity of the proposed method. Several benchmark face databases are adopted to evaluate the performance. From the results, the proposed method achieves 70% recognition rates.

參考文獻
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