计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 111-117.doi: 10.11896/jsjkx.190500004
高玉潼1,2, 雷为民1, 原玥2
GAO Yu-tong1,2, LEI Wei-min1, YUAN Yue2
摘要: 在现代社会,人脸目标识别技术在各大领域应用得越来越广泛;同时,社会治安环境和国际安全问题也愈发严峻,人脸目标识别面临着越来越严峻的挑战。在复杂环境下,检测目标和背景场景都是复杂且动态变化的,传统的人脸目标识别技术已无法满足日益增长的需求。对此,文中通过聚类分析方法对传统SIFT(Scale Invariant Feature Transform)算法进行优化改进,利用聚类分析的原理将对象特征点进行归类,使得聚类结果更加符合设定阈值,从而提高匹配效率。为了验证优化改进后算法的匹配效果,将改进后的算法和传统SIFT算法进行对比检测分析。结果表明,改进后的SIFT算法能够消除无关书籍的干扰,实现图像匹配点的完整连接。为了验证改进算法的有效性,基于几个常用库将其与常用算法进行对比分析,结果显示聚类SIFT算法在CASPEAL-R1,CFP,Multi-PIE方面都要优于其他算法,具有更好的应用效果和适用性。
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