A mobilized automatic human body measure system using neural network

Xia, Likun; Yang, Jian; Han, Tao; Xu, Haibo; Yang, Qi; Zhao, Yitian; Wang, Yongtian

doi:10.1007/s11042-018-6645-6

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…This is absolute value will be highly discriminating too. There are certain experiments like [177,194,204] where these anthropometric and geometric measurements of human are used for recognition. However, these experiments are performed on smaller datasets with limited diversity.…”

Section: Methods For Quantitative Annotationsmentioning

confidence: 99%

Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Section: Methods For Quantitative Annotationsmentioning

confidence: 99%