目前，如何使人臉辨識系統具有快速且準確的辨識效果是值得研究的方向， 透過局部顯著的特徵和有效減少比對次數的分類方法為解決上述問題的方案。現 今人臉辨識系統多為提取整個人臉圖像特徵，接著逐一與資料庫中的圖像進行比 對，以獲得辨識結果。在本文中，提出一種篩選技術，能有效避免比對特徵過大 和比對次數過多的問題，為了設計出最佳的篩選技術，以變異數分析探討局部顯 著特徵對辨識率的影響，以獲得最佳的篩選技術流程。結果顯示所提出的篩選技 術與原始系統相比，在Extended Yale Face Database B 與MECL 人臉資料庫當中， 不僅具有相同的辨識率，在辨識時間上更提升了112.5%與52.9%的效率。故證實 篩選技術不僅擁有相同的辨識效果，還能大幅的降低辨識時間。

Nowadays, how to make the face recognition faster and more accurate is one of the pursuing targets in this field. The target can be achieved through a local significant feature and effectively reducing the number of comparisons. Currently, most of the face recognition methods are used to extract the features of the entire face image, and through one-by-one comparisons with the images in the database to obtain final results. In this study, a screening technique is proposed that can effectively improve the defects of over-compared features and excessive comparing times. In order to design this screening technology, the influence of locally significant features on the recognition rate is explored by using variance analysis to obtain the optimal screening technology process. The studied results show that to compare with the current methods, the proposed technology not only can maintain the same recognition rate, but also can improve the recognition efficiencies upon 115.5% and 52.9% on the recognition time subject to the face databases of Extended Yale Face Database B and MECL, respectively.