Abstract
In this paper, we propose a new view-invariant framework for gait analysis. The framework profits from the dimensionality reduction advantages of Direct Linear Discriminant Analysis (DLDA) to build a unique view-invariant model. Among these advantages is the capability to tackle the under-sampling problem (USP), which commonly occurs when the number of dimensions of the feature space is much larger than the number of training samples. Our framework employs Gait Energy Images (GEIs) as features to create a single joint model suitable for classification of various angles with high accuracy. Performance evaluations shows the advantages of our framework, in terms of computational time and recognition accuracy, as compared to state-of-the-art view-invariant methods.
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Notes
- 1.
Our end-to-end implementation is available in: https://yadi.sk/d/MuEE2_tGjJxcq.
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This work has been financed by Consejo Nacional de Ciencia y Tecnologia (CONACyT), Mexico and by Secretaria de Educacion Publica, Mexico.
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Portillo, J. et al. (2016). View-Invariant Gait Recognition Using a Joint-DLDA Framework. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_34
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