典型文献
Research on will-dimension SIFT algorithms for multi-attitude face recognition
文献摘要：
The results of face recognition are often inaccurate due to factors such as illumination, noise in-tensity, and affine/projection transformation. In response to these problems, the scale invariant fea-ture 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 extrac-ted in advance by the SIFT algorithm; then the high-dimensional data is projected into the low-di-mensional space to remove the redundant feature points, thereby changing the way of generating fea-ture 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.
文献关键词：
中图分类号：
作者姓名：
SHENG Wenshun;SUN Yanwen;XU Liujing
作者机构：
Pujiang Institute,Nanjing Tech University,Nanjing 211200,P.R.China;School of Information Engineering,Nanjing Audit University,Nanjing 211815,P.R.China
文献出处：
引用格式：
[1]SHENG Wenshun;SUN Yanwen;XU Liujing-.Research on will-dimension SIFT algorithms for multi-attitude face recognition)[J].高技术通讯（英文版）,2022(03):280-287
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AB值：
0.555951
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