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Estimating Anthropometric Soft Biometrics: An Empirical Method

Bilal Hassan1,*, Hafiz Husnain Raza Sherazi2, Mubashir Ali3, Yusra Siddiqi2

1 Engineering & Environment, Northumbria University, London Campus, London, E1 7HT, UK
2 School of Computing and Engineering, University of West London, London, W5 5RF, UK
3 School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

* Corresponding Author: Bilal Hassan. Email: email

(This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)

Intelligent Automation & Soft Computing 2023, 37(3), 2727-2743. https://doi.org/10.32604/iasc.2023.039275

Abstract

Following the success of soft biometrics over traditional biometrics, anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video. Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics. However, one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video. The level of accuracy is usually affected by several contextual factors such as overlapping body components, an angle from the camera, and ambient conditions. Exploring and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or retrieval. For this purpose, anthropometric soft biometrics, which are originally geometric measurements of the human body, can be computed with ease and higher accuracy using landmarks information from the human body. To this end, several key contributions are made in this paper; i) summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body, ii) a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field, iii) an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system, and iv) finally, a novel bag of anthropometric soft biometrics containing a list of anthropometrics is presented those are practically possible to measure from an image or video. To the best of our knowledge, anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained and unconstrained scenarios and they also minimize the approximation level of feature value estimation than traditional biometrics. In our opinion, anthropometric soft biometrics constitutes a practical approach for recognition using closed-circuit television (CCTV) or retrieval from the image dataset, while the bag of anthropometric soft biometrics presented contains a potential collection of biometric features which are less sensitive to contextual factors.

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APA Style
Hassan, B., Sherazi, H.H.R., Ali, M., Siddiqi, Y. (2023). Estimating anthropometric soft biometrics: an empirical method. Intelligent Automation & Soft Computing, 37(3), 2727-2743. https://doi.org/10.32604/iasc.2023.039275
Vancouver Style
Hassan B, Sherazi HHR, Ali M, Siddiqi Y. Estimating anthropometric soft biometrics: an empirical method. Intell Automat Soft Comput . 2023;37(3):2727-2743 https://doi.org/10.32604/iasc.2023.039275
IEEE Style
B. Hassan, H.H.R. Sherazi, M. Ali, and Y. Siddiqi "Estimating Anthropometric Soft Biometrics: An Empirical Method," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 2727-2743. 2023. https://doi.org/10.32604/iasc.2023.039275



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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