Performance Assessment of Soft Biometrics Technologies for Border Crossing

Hassan, Bilal; Izquierdo, Ebroul; Chandramouli, Krishna

doi:10.1002/9781119834175.ch4

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…As mentioned earlier, human body anthropometrics emerged as a key research domain, and it achieved significant success in recognition scenarios [39]. We already discussed several research experiments with the aim of extracting or estimating anthropometric features from the images.…”

Section: Bag Of Anthropometric Soft Biometricsmentioning

confidence: 99%

Estimating Anthropometric Soft Biometrics: An Empirical Method

Hassan,

Husnain Raza Sherazi,

Ali

et al. 2023

Intelligent Automation &Amp; Soft Computing

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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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Section: Bag Of Anthropometric Soft Biometricsmentioning

confidence: 99%

Estimating Anthropometric Soft Biometrics: An Empirical Method

Hassan,

Husnain Raza Sherazi,

Ali

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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A multi-channel soft biometrics framework for seamless border crossings

Hassan

Sherazi

Ali

et al. 2023

EURASIP J. Adv. Signal Process.

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As the number of passengers at border entry points such as airports and rail stations increases, so does the demand for seamless, secure, and fast biometric technologies for verification purposes. Although fingerprints are currently useful biometric technologies, they are intrusive and slow down the end-to-end verification process, increasing the chances of tampering. Emerging as an alternative technology, soft biometrics have proven successful for non-intrusive and rapid verification. Soft biometrics consists of a large set of features from three different modalities of the human body, including the face, body, and essential & auxiliary attachments. This paper proposes a multi-channel soft biometrics framework that leverages soft biometrics technology over traditional biometrics. The framework encapsulates four distinct components: ApparelNet, which verifies essential and auxiliary attachments; A-Net, which measures anthropometric soft biometrics; OneDetect, which predicts global soft biometrics; and RSFS, which develops a set of highly relevant and supportive soft biometrics for verification. The proposed framework addresses several critical limitations of existing biometrics technologies during the verification process at border entry points, such as intrusive behavior, response time, biometric tampering, and privacy issues. The proposed multi-channel soft biometrics framework has been evaluated using several benchmark datasets in the field, such as Front-view Gait (FVG), Pedestrian Attribute Recognition At Far Distance (PETA), and Multimedia and Vision (MMV) Pedestrian. Using heterogeneous datasets enables the testing of each framework component or channel against numerous constrained and unconstrained scenarios. The outcome of the envisioned multi-channel soft biometrics framework is presented based on distinct outcomes from each channel, but it remains focused on determining a single cumulative verification score for verification at border control. In addition, this multi-channel soft biometrics framework has extended applications in several fields, including crowd surveillance, the fashion industry, and e-learning.

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Performance Assessment of Soft Biometrics Technologies for Border Crossing

Cited by 2 publications

References 59 publications

Estimating Anthropometric Soft Biometrics: An Empirical Method

Estimating Anthropometric Soft Biometrics: An Empirical Method

A multi-channel soft biometrics framework for seamless border crossings

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