Abstract
In this paper we propose a person identification methodology from frontal standing posture using only skeleton information obtained from Kinect. In the first stage, features related to the physical characteristic of a person are calculated for every frame and then noisy frames are removed based on these features using unsupervised learning based approach. We have also proposed 6 new angle and area related features along with the physical build of a person for the supervised learning based identification. Experimental results indicate that the proposed algorithm is able to achieve 96% recognition accuracy and outperforms all the stat-of-the-art methods suggested by Sinha et al. and Preis et al.
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Keywords
- Support Vector Machine
- Structural Risk Minimization
- Gait Recognition
- Multiclass Support Vector Machine
- Distance Base Outlier
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Chakravarty, K., Chattopadhyay, T. (2014). Frontal-Standing Pose Based Person Identification Using Kinect. In: Kurosu, M. (eds) Human-Computer Interaction. Advanced Interaction Modalities and Techniques. HCI 2014. Lecture Notes in Computer Science, vol 8511. Springer, Cham. https://doi.org/10.1007/978-3-319-07230-2_21
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DOI: https://doi.org/10.1007/978-3-319-07230-2_21
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