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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis
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A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

机译：一种基于联合直接线性判别分析的视图不变步态识别算法

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摘要

This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.
机译：本文提出了一种视图不变的步态识别算法，其构建了利用直接线性判别分析（DLDA）提供的维数减少的独特视图不变模型。 所提出的方案能够减少通常在训练样本的数量小于特征空间的尺寸时出现的欠采样问题（USP）。 所提出的方法使用步态能量图像（GEIS）和DLDA来创建一个视图不变模型，可以高精度地确定在分析的分析中的身份，独立于传入角度。 评价结果表明，与其他先前提出的步态识别方法在计算复杂性和识别准确性方面，该方案提供了完全独立于视角和更高的准确性的识别性能。

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