Please use this identifier to cite or link to this item: http://dspace.aiub.edu:8080/jspui/handle/123456789/2103
Title: A Novel Contactless Middle Finger Knuckle Based Person Identification Using Ensemble Learning
Authors: Dey, Noboranjan
M, Srinivas
R.B.V., Subramanyam
Keywords: Deep Learning
Image Processing
Biometrics
Issue Date: 22-Nov-2023
Publisher: IEEE
Series/Report no.: ;981-986
Abstract: In Modern times, automated security for identifying a person is one of the main concerns. There is a significant need for a trustworthy and secure identity verification solution. A reliable way to identify someone can be using a biometric identification system. The finger knuckle pattern offers excellent discriminatory features for biometric identification with indirect touch, including the advantages of long-range visibility. Existing models are failing to handle the depth information in finger knuckles that are highly relevant to understand the identification patterns. Therefore, we elaborate on the significance of utilizing the middle finger knuckle for biometric identification. We propose an ensemble approach that appropriately captures the rich features to identify a person based on their finger knuckle. The proposed model performance is evaluated on a standard dataset (HKPolyU 3D photometric stereo knuckle image dataset). Experimental results illustrate that the proposed model outperforms the existing results. Further, this approach would be advantageous in forensic investigations, security, and surveillance.
URI: https://ieeexplore.ieee.org/document/10322449
http://dspace.aiub.edu:8080/jspui/handle/123456789/2103
ISBN: 979-8-3503-0219-6
ISSN: 2159-3450
Appears in Collections:Publications: Conference

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