Uncooperative Gait Recognition Using Joint Bayesian
| Autor: | Chao Li, Shouqian Sun, Kan Qiao, Xin Min, Xiaoyan Pang |
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| Rok vydání: | 2017 |
| Předmět: |
Protocol (science)
Biometrics business.industry Computer science Bayesian probability 02 engineering and technology Variance (accounting) 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Test (assessment) Identification (information) Gait (human) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Joint (audio engineering) business computer 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319716060 ICIG (1) |
| DOI: | 10.1007/978-3-319-71607-7_11 |
| Popis: | Human gait, as a soft biometric, helps to recognize people by walking without subject cooperation. In this paper, we propose a more challenging uncooperative setting under which views of the gallery and probe are both unknown and mixed up (uncooperative setting). Joint Bayesian is adopted to model the view variance. We conduct experiments to evaluate the effectiveness of Joint Bayesian under the proposed uncooperative setting on OU-ISIR Large Population Dataset (OULP) and CASIA-B Dataset (CASIA-B). As a result, we confirm that Joint Bayesian significantly outperform the state-of-the-art methods for both identification and verification tasks even when the training subjects are different from the test subjects. For further comparison, the uncooperative protocol, experimental results, learning models, and test codes are available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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