一种改善人脸识别效率的快速筛选方法研究
Study on the Faster Novel Screening Technology for Improving the Efficiency of Face Recognition

人脸识别是一种重要的身份鉴别技术，具有广泛的应用前景。目前，如何使人脸识别系统具有快速且准确的识别效果是值得研究的方向，通过局部显著的特征和有效减少比对次数的分类方法为解决上述问题的方案。现今人脸识别系统多为提取整个人脸图像特征，接着逐一与数据库中的图像进行比对，以获得识别结果。在本文中，提出一种筛选技术，能有效避免比对特征过大和比对次数过多的问题，为了设计出最佳的筛选技术，以变异数分析探讨局部显著特征对识别率的影响，以获得最佳的筛选技术流程。结果显示所提出的筛选技术与原始系统相比，在Extended Yale Face Database B与MECL人脸数据库当中，不仅具有相同的识别率，在识别时间上更提升了105.8%与50%的效率。故证实筛选技术不仅拥有相同的识别效果，还能大幅的降低识别时间。
Face recognition is an important identification technology and has a wide application prospect. 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.