人臉辨識演算法通常必須克服一些問題，如角度、光線以及表情變化。為了降低這些問題的影響，已經有許多學者不斷地研究如何在特徵空間中尋找最佳的線性或非線性區別轉換方式以達到較佳的辨識結果。在本研究中，我們提出了一種基於最近特徵空間轉換法的人臉辨識演算法。這種方法是將一個資料點與其最鄰近的資料所構成的直線或是空間，將這一段距離嵌入到區別分析中。透過這樣的方式，可以將類別資訊、區域結構拓樸保留以及最近特徵空間測量這三個因子嵌入到最佳化的區別分析中。在實驗結果中，我們的演算法與其它著名演算法在比較後都有較佳的辨識結果。 Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce these impacts, many researchers have been trying to find the best discriminant transformation in feature spaces, either linear or nonlinear, to obtain better recognition. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or an NFS is embedded in the transformation in the discriminant analysis. Three factors, namely class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated using several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.
