Portillo-Portillo Jose, Leyva Roberto, Sanchez Victor, Sanchez-Perez Gabriel, Perez-Meana Hector, Olivares-Mercado Jesus, Toscano-Medina Karina, Nakano-Miyatake Mariko
Instituto Politécnico Nacional, ESIME Culhuacan, 04430 Coyoacán, CDMX, Mexico.
Department of Computer Science, University of Warwick, CV4 7AL Coventry, UK.
Sensors (Basel). 2016 Dec 22;17(1):6. doi: 10.3390/s17010006.
This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework's computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.
本文提出了一种视图不变步态识别框架，该框架采用了一种独特的视图不变模型，该模型受益于直接线性判别分析（DLDA）提供的降维。该框架采用步态能量图像（GEI），创建了一个单一的联合模型，能够准确地对在不同角度捕获的GEI进行分类。此外，所提出的框架还有助于减少通常在训练样本数量远小于特征空间维度时出现的欠采样问题（USP）。评估实验将所提出框架的计算复杂度和识别准确率与其他视图不变方法进行了比较。结果表明，在计算复杂度和识别准确率方面均有提高。
