| SHENG Wenshun (圣文顺)*,SUN Yanwen**,XU Liujing*.[J].高技术通讯(英文),2022,28(3):280~287 |
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| Research on will-dimension SIFT algorithms for multi-attitude face recognition |
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| DOI：10.3772/j.issn.1006-6748.2022.03.007 |
| 中文关键词: |
| 英文关键词: face recognition, scale invariant feature transformation (SIFT), dimensionality reduction, principal component analysis-scale invariant feature transformation (PCA-SIFT) |
| 基金项目: |
| Author Name | Affiliation | | SHENG Wenshun (圣文顺)* | (*Pujiang Institute, Nanjing Tech University, Nanjing 211200, P.R.China)
(**School of Information Engineering, Nanjing Audit University, Nanjing 211815, P.R.China) | | SUN Yanwen** | (*Pujiang Institute, Nanjing Tech University, Nanjing 211200, P.R.China)
(**School of Information Engineering, Nanjing Audit University, Nanjing 211815, P.R.China) | | XU Liujing* | (*Pujiang Institute, Nanjing Tech University, Nanjing 211200, P.R.China)
(**School of Information Engineering, Nanjing Audit University, Nanjing 211815, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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