Persistent homology-based gait recognition robust to upper body variations
| Autor: | Edel García-Reyes, Rocio Gonzalez-Diaz, Javier Lamar-Leon, Raul Alonso-Baryolo |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII), Ministerio de Economía y Competitividad (MINECO). España |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Persistent homology
Biometrics business.industry Computer science Upper body 010102 general mathematics GAIT complex Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 01 natural sciences Gait ComputingMethodologies_PATTERNRECOGNITION Gait (human) Robustness (computer science) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence 0101 mathematics business |
| Zdroj: | ICPR idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| ISSN: | 2015-6707 |
| Popis: | Gait recognition is nowadays an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. However, when the upper body movements are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat, the reported results show low accuracy. With the goal of solving this problem, we apply persistent homology to extract topological features from the lowest fourth part of the body silhouettes. To obtain the features, we modify our previous algorithm for gait recognition, to improve its efficacy and robustness to variations in the amount of simplices of the gait complex. We evaluate our approach using the CASIA-B dataset, obtaining a considerable accuracy improvement of 93:8%, achieving at the same time invariance to upper body movements unrelated with the dynamic of the gait. Ministerio de Economía y Competitividad MTM2015-67072-P |
| Databáze: | OpenAIRE |
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