This paper proposes a novel multi-kernel fuzzy-based local Gabor binary patterns method (MFLGBP) for the purpose of gait representation and recognition. First, we construct the gait energy image (GEI) from mean motion cycle of a gait sequence. Then, we apply Gabor filters and encode the variations in the Gabor magnitude by using a kernel-based fuzzy local binary pattern (KFLBP) operator. Finally, classification is performed using a support vector machine (SVM). Experiments are carried out using the benchmark CASIA B gait database. Our proposed feature extraction method has shown promising performance in terms of correct recognition rate as compared to other methods.
CITATION STYLE
Binsaadoon, A. G., & El-Alfy, E. S. M. (2016). Multi-kernel fuzzy-based local gabor patterns for gait recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10072 LNCS, pp. 790–799). Springer Verlag. https://doi.org/10.1007/978-3-319-50835-1_71
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