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Gait recognition from corrupted silhouettes: a robust statistical approach

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Abstract

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.

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Notes

  1. This scheme admits a series of half cycles.

  2. In the case of GEI, \(v(\cdot )\) computes normalized Euclidean distances between vectors of grayscale pixel values, thus \(v(\cdot )\in [0,255]\), while in GHEI and GEI \(+\) HoG, \(v(\cdot )\) measures absolute differences between features normalized in [0, 1].

  3. This is a particular case of having at least four cycles.

  4. Note that each series includes results from both defective and clean scenarios together.

  5. Performance curves from the other two methods are pretty similar to those from GEI showed in this section.

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Acknowledgments

This work has been supported by the grants P1-1B2012-22 and PREDOC/2012/05 from Universitat Jaume I, PROMETEOII/2014/062 from Generalitat Valenciana, and TIN2013-46522-P from Spanish Ministry of Economy and Competitiveness.

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Authors and Affiliations

  1. Institute of New Imaging Technologies, Universitat Jaume I, Castellón de la Plana, Spain

    Javier Ortells, Ramón A. Mollineda & Raúl Martín-Félez

  2. Departamento de Física y Matemática, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, México

    Boris Mederos

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  1. Javier Ortells
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  2. Ramón A. Mollineda
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  3. Boris Mederos
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  4. Raúl Martín-Félez
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Correspondence to Javier Ortells.

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Ortells, J., Mollineda, R.A., Mederos, B. et al. Gait recognition from corrupted silhouettes: a robust statistical approach. Machine Vision and Applications 28, 15–33 (2017). https://doi.org/10.1007/s00138-016-0798-y

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