Abstract
Accurate gait recognition is of high significance for numerous industrial and consumer applications, including virtual reality, online games, medical rehabilitation, video surveillance, and others. This paper proposes multi-layer perceptron (MLP) based neural network architecture for human gait recognition. Two unique geometric features: joint relative cosine dissimilarity (JRCD) and joint relative triangle area (JRTA) are introduced. These features are view and pose invariant, and thus enhance recognition performance. MLP model is trained using dynamic JRTA and JRCD sequences. The performance of the proposed MLP architecture is evaluated on publicly available 3D Kinect skeleton gait database and is shown to be superior to other state-of-the-art methods.
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Acknowledgments
Authors would like to acknowledge partial support from NSERC DG “Machine Intelligence for Biometric Security”, NSERC ENGAGE on Gait Recognition and NSERC SPG on Smart Cities funding.
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Hossain Bari, A.S.M., Gavrilova, M.L. (2019). Multi-layer Perceptron Architecture for Kinect-Based Gait Recognition. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_31
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DOI: https://doi.org/10.1007/978-3-030-22514-8_31
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