Person recognition across multi-session and multi-exemplar images using ensemble of classifiers

Bilal Hassan; Ebroul Izquierdo

doi:10.1117/12.2600892

30 June 2021 Person recognition across multi-session and multi-exemplar images using ensemble of classifiers

Bilal Hassan, Ebroul Izquierdo

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187807 (2021) https://doi.org/10.1117/12.2600892
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Person recognition over time is a bit challenging task as compared to re-identification in multi-camera environment. Usually, people appear after certain time period at public places like airports, carrying accessories and changing of clothes etc. In this paper, we proposed a newer recognition framework using two types of images i.e. whole and upper body silhouette. A customized version of DeepLabv3 is used for human body semantic segmentation. The Generic Fourier Descriptor (GFD) based feature set is fed to One-Vs-Rest schema in ensemble of K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers. The experiments are carried out on Front-View Gait (FVG) dataset recorded in year 2017 and 2018 respectively. An overall recognition accuracy of more than 93% is noted using both classifiers on whole body human silhouette images. On the other hand, upper half human silhouette obtained recognition accuracy of more than 91% and 88% using RF and KNN respectively. Code is available at https://git.io/JtfMY

Citation Download Citation

Bilal Hassan and Ebroul Izquierdo "Person recognition across multi-session and multi-exemplar images using ensemble of classifiers", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187807 (30 June 2021); https://doi.org/10.1117/12.2600892

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