Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization

Vyas, Ritesh and Williams, Bryan M. and Rahmani, Hossein and Boswell-Challand, Ricki and Jiang, Zheheng and Angelov, Plamen and Black, Sue (2022) Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization. Sensors, 22 (4). ISSN 1424-8220

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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.

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23 Feb 2022 10:45
Last Modified:
13 Dec 2022 11:35