Mr. Bilal Hassan Profile

Bilal Hassan

at Queen Mary Univ of London

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Proceedings Article | 12 October 2022 Paper

RSFS: A soft biometrics-based relative support features set for person verification

Bilal Hassan, Ebroul Izquierdo

Proceedings Volume 12342, 1234202 (2022) https://doi.org/10.1117/12.2644457

KEYWORDS: Biometrics, Data modeling, Feature extraction, Inspection, Skin, Head, Surveillance, Multimedia, Machine learning, Image resolution

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Generally, biometrics is gaining increased attention due to its application for secure and efficient verification – more specifically at border crossing points. Usually, there are many different types of biometrics associated with human body i.e., intrusive like finger prints etc. and non-intrusive, termed as soft biometrics. In order to make the concept of Smart Borders a reality, the non-intrusive soft biometrics are the baseline technology. One of biggest challenge in soft biometrics based verification is to find a highly related set of features from different modalities of human body – as there is large number such soft biometrics associated with human body. In fact, this is extremely useful to select only those soft biometrics which are supportive to each other and relevant to the problem domain. In our work, we thoroughly investigated one of the largest collection of soft biometrics and developed a multiple non-linear regression based framework for the selection of highly supportive and relevant soft biometrics. We used one of the largest dataset e.g., PETA and its annotation for the evaluation of our proposed model. The accuracy is reported in form of MAE and error distribution graphs for two global soft biometrics i.e., gender and age prediction.

Proceedings Article | 30 June 2021 Paper

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

Bilal Hassan, Ebroul Izquierdo

Proceedings Volume 11878, 1187807 (2021) https://doi.org/10.1117/12.2600892

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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

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