face identification that based on tensor SVD decomposition

最新推荐文章于 2022-03-15 23:29:09 发布

原创最新推荐文章于 2022-03-15 23:29:09 发布 · 295 阅读

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本文提出了一种基于张量奇异值分解(SVD)的人脸识别方法，在人脸识别过程中改进了传统主成分分析(PCA)等算法对图像条件的过度依赖。通过处理三维线性数据模型，该方法能在不同条件下保持较高的识别精度。实验结果表明，该算法不仅准确性高且稳定性强，特别是在数据量增大时效率显著提升。

Abstract: By using an approach based on the SVD(Tensor Singular Value Decomposition) in the extraction and expression of human face features in the process of face recognition, the precedent algorithms, such as the PCA(Principal Component Analysis) which has excessive dependence on the condition of face pictures, are improved. The SVD method deals with three-dimensional linear data model, which can avoid the decrease of precision caused by the variation of picture conditions when using method deals with two-dimensional linear data model, and provides a relatively stable result despite the change of conditions. In addition, by using QR decomposition of matrix to reduce the complexity of calculation without jeopardizing the accuracy, the algorithm is optimized efficiently. Four groups of experiments based on Matlab are conducted, and the results are analyzed in comparison with those from the PCA method, which verifies the outstanding correctness and stability of the algorithm under varying conditions. Meanwhile, experiments on the optimized algorithm show a remarkable improvement of efficiency compared to the basic algorithm, especially when the data amount gets larger.

The recognition result of my algorithm using yale A database.We can see most people's photo are correctly math