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.
作者
圣文顺
SUN Yanwen
XU Liujing
SHENG Wenshun;SUN Yanwen;XU Liujing(Pujiang Institute,Nanjing Tech University,Nanjing 211200,P.R.China;School of Information Engineering,Nanjing Audit University,Nanjing 211815,P.R.China)
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)。