Research on will-dimension SIFT algorithms for multi-attitude face recognition

作     者：圣文顺 SUN Yanwen XU Liujing SHENG Wenshun;SUN Yanwen;XU Liujing

作者机构：Pujiang InstituteNanjing Tech UniversityNanjing 211200P.R.China School of Information EngineeringNanjing Audit UniversityNanjing 211815P.R.China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2022年第28卷第3期

页      面：280-287页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China (No.61571222) the Natural Science Research Program of Higher Education Jiangsu Province (No.19KJD520005) Qing Lan Project of Jiangsu Province (Su Teacher’s Letter 2021 No.11) Jiangsu Graduate Scientific Research Innovation Program (No.KYCX21_1944)。 

主　　题：face recognition scale invariant feature transformation(SIFT) dimensionality reduction principal component analysis-scale invariant feature transformation(PCA-SIFT) 

摘      要：The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.