Department of Computer Scienceand Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan, R.O.C.
IEEE Trans Pattern Anal Mach Intell. 2011 Jun;33(6):1073-86. doi: 10.1109/TPAMI.2010.197.
Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. 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 the NFS is embedded in the transformation through the discriminant analysis. Three factors, including 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 by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.
人脸识别算法通常需要解决面部姿势、光照和表情（PIE）等问题。为了降低这些影响，许多研究人员一直在尝试在特征空间中找到最佳的鉴别变换，无论是线性的还是非线性的，以获得更好的识别结果。各种研究人员还设计了新颖的匹配算法来降低 PIE 效应。在这项研究中，提出了一种用于人脸识别的最近特征空间嵌入（称为 NFS 嵌入）算法。通过判别分析，将点与最近特征线（NFL）或 NFS 的距离嵌入到变换中。为了在特征空间中找到最有效和最具鉴别力的变换，考虑了三个因素，包括类可分离性、邻域结构保持和 NFS 度量。该方法在几个基准数据库上进行了评估，并与几种最新算法进行了比较。根据比较结果，该方法优于其他算法。
