A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

作者：Portillo Portillo Jose; Leyva Roberto; Sanchez Victor; Sanchez Perez Gabriel; Perez Meana Hector; Olivares Mercado Jesus; Toscano Medina Karina; Nakano Miyatake Mariko

来源：Applied Intelligence, 2018, 48(5): 1200-1217.

DOI：10.1007/s10489-017-1043-8

• Gait recognition scheme
• View-invariant method
• Gait energy image
• Direct linear discriminant analysis (DLDA)
• Euclidean distance

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.

• 出版日期2018-5