Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization

Vyas, Ritesh, Williams, Bryan M ORCID: 0000-0001-5930-287X, Rahmani, Hossein, Boswell-Challand, Ricki, Jiang, Zheheng, Angelov, Plamen and Black, Sue (2022) Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization. SENSORS, 22 (4). 1569-.

Access the full-text of this item by clicking on the Open Access link.

Abstract

The knuckle creases present on the dorsal side of the human hand can play significant role in identifying the offenders of serious crime, especially when evidence images of more recognizable biometric traits, such as the face, are not available. These knuckle creases, if localized appropriately, can result in improved identification ability. This is attributed to ambient inclusion of the creases and minimal effect of background, which lead to quality and discerning feature extraction. This paper presents an ensemble approach, utilizing multiple object detector frameworks, to localize the knuckle regions in a functionally appropriate way. The approach leverages from the individual capabilities of the popular object detectors and provide a more comprehensive knuckle region localization. The investigations are completed with two large-scale public hand databases which consist of hand-dorsal images with varying backgrounds and finger positioning. In addition to that, effectiveness of the proposed approach is also tested with a novel proprietary unconstrained multi-ethnic hand dorsal dataset to evaluate its generalizability. Several novel performance metrics are tailored to evaluate the efficacy of the proposed knuckle localization approach. These metrics aim to measure the veracity of the detected knuckle regions in terms of their relation with the ground truth. The comparison of the proposed approach with individual object detectors and a state-of-the-art hand keypoint detector clearly establishes the outperforming nature of the proposed approach. The generalization of the proposed approach is also corroborated through the cross-dataset framework.

Item Type:	Article
Uncontrolled Keywords:	knuckle localization, object detector, ensemble, forensics
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	16 May 2022 12:36
Last Modified:	18 Jan 2023 21:02
DOI:	10.3390/s22041569
Open Access URL:	https://www.mdpi.com/1424-8220/22/4/1569
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3154901