在已有的人脸识别方法中,识别时间一般随数据库规模而线性增长,因而难以控制,影响算法对大型人脸数据库的实用性.为此,提出了一种新的人脸识别策略:首先通过训练确定一组有代表性人脸作为参考脸,并在此基础上建立基于相似度的排序结构;这样在识别时只需计算待识别人脸与参考脸的相似度,即可结合排序结构确定一组最相似人脸,再逐个比较确定最终识别结果.由于大量计算已离线完成,识别速度大大提高.通过选择参考脸和最相似人脸的数目,可大致控制不同规模数据库的识别时间.实验表明,该识别策略下识别性能略有下降,但识别速度大大提高.

In the existing face recognition methods, recognition time usually increases linearly with the scale of face databases, which will certainly make the recognition algorithms less effective in practice. A novel face recognition scheme is presented. Firstly, a group of typical faces is selected, called reference faces, as comparison foundation through clustering in the eigenspace. Then, a similarity sorting space is built up based on the similarities with reference faces. For recognition, only need to compute the similarity of the input face with the reference faces to pick up the most similar faces in the sorting space quickly, then to compute the similarity with these picked up faces to determine the final recognition result. Since a lot of calculation is done offline, the recognition speed is improved greatly. Through selecting the number of reference faces and the mostly expected similar face set, the recognition time for different scales of databases can be controlled approximately. Experiments show that the recognition capability of out scheme decreases a little than ordinary methods,but the recognition speed is increased greatly.