Gait recognition from corrupted silhouettes: a robust statistical approach

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• 作者：Javier Ortells ; Ramón A. Mollineda ; Boris Mederos…
• 关键词：Gait recognition ; Model ; free ; Noisy silhouettes ; Occluded silhouettes ; Robust statistics
• 刊名：Machine Vision and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：1-2
• 页码：15-33
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1769
• 卷排序：28

文摘

This paper introduces a method based on robust statistics to build reliable gait signatures from averaging silhouette descriptions, mainly when gait sequences are affected by severe and persistent defects. The term robust refers to the ability of reducing the impact of silhouette defects (outliers) on the average gait pattern, while taking advantage of clean silhouette regions. An extensive experimental framework was defined based on injecting three types of realistic defects (salt and pepper noise, static occlusion, and dynamic occlusion) to clean gait sequences, both separately in an easy setting and jointly in a hard setting. The robust approach was compared against two other operation modes: (1) simple mean (weak baseline) and (2) defect exclusion (strong benchmark). Three gait representation methods based on silhouette averaging were used: Gait Energy Image (GEI), Gradient Histogram Energy Image (GHEI), and the joint use of GEI and HOG descriptors. Quality of gait signatures was assessed by their discriminant power in a large number of gait recognition tasks. Nonparametric statistical tests were applied on recognition results, searching for significant differences between operation modes.