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首页> 外文期刊>Indian Journal of Science and Technology >An Improved Face Recognition based on Scale Invariant Feature Transform (SIFT): Training for Integrating Multiple Images and Matching by Key Point’s Descriptor-Geometry
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An Improved Face Recognition based on Scale Invariant Feature Transform (SIFT): Training for Integrating Multiple Images and Matching by Key Point’s Descriptor-Geometry

机译：一种基于尺度不变特征变换（SIFT）的改进的人脸识别：训练集成多张图像并按关键点的描述符几何进行匹配

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摘要

Objectives: We proposed for reduction of the computational complexity and improvement of the recognition precision in the face recognition system using Scale Invariant Feature Transform (SIFT) local feature approaches. Methods/Statistical Analysis: The first one is a novel training procedure for the integration of multiple training images. This training procedure performs to remove redundant local features and blend different local features from face image. The second one is a proposed matching scheme not only considering the similarity of key point’s descriptor but also the geometry property through one-to-one matching between query and references images. Findings: This research finds the optimal settings of parameter for the proposed face recognition system based on SIFT. First, we have analyzed the change of recognition rate according to the resolution of face image in the proposed system. Then, to effect of the reduced number of key points per subject on the recognition rate and the resolution of face image were analyzed with the multiple templates per subject. As a result, we observed that the proposed template training procedure using Lowe’s key points detection method with 50×61 resolution of face images achieves higher recognition rate than the holistic approaches. The usage of Geng’s key point detection method in the proposed system obtains higher recognition rate than the usage of Lowe’s method. From the experimental result with ORL databases, the proposed face recognition system gives 99.5% of rate, which shows the higher performance than the previous ones. In addition, the proposed integration method of multiple training images reduces the number of key points by average 49.84% than the method of using multiple templates. Improvements/Applications: The experimental result of the proposed system in two well-known face databases shows that the computational quantities are reduced effectively compared to other SIFT based methods, and it gives better performance on face recognition accuracy.
机译：目的：我们提出了使用尺度不变特征变换（SIFT）局部特征方法来降低人脸识别系统的计算复杂度并提高其识别精度。方法/统计分析：第一个是用于整合多个训练图像的新颖训练程序。该训练过程执行以去除多余的局部特征并从面部图像中融​​合不同的局部特征。第二种是提出的匹配方案，不仅考虑关键点描述符的相似性，而且还通过查询和参考图像之间的一对一匹配来考虑几何属性。结果：本研究为基于SIFT的拟人脸识别系统找到了最佳参数设置。首先，我们在提出的系统中根据面部图像的分辨率分析了识别率的变化。然后，为了减少每个主题的关键点数量对识别率和面部图像分辨率的影响，使用每个主题的多个模板进行了分析。结果，我们观察到，采用劳氏关键点检测方法的人脸图像分辨率为50×61的拟议模板训练程序比整体方法具有更高的识别率。在建议的系统中使用Geng的关键点检测方法比使用Lowe的方法获得更高的识别率。根据ORL数据库的实验结果，提出的人脸识别系统的识别率达到了99.5％，显示出比以前更高的性能。另外，与使用多个模板的方法相比，所提出的多个训练图像的集成方法将关键点的数量平均减少了49.84％。改进/应用：该系统在两个著名的人脸数据库中的实验结果表明，与其他基于SIFT的方法相比，该算法有效地减少了计算量，并且在人脸识别精度上具有更好的性能。

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