Person recognition across multi session and multi exemplar images using ensemble of classifiers
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
International Conference on Digital Image Processing