Finger Knuckle Print Identification using Gabor Features

Neware, Shubhangi; Mehta, Kush P.; Zadgaonkar, A. S.

doi:10.5120/17283-7641

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…The recognition of this difference in patterning has led to a significant volume of research in the biometric and security‐based literature and the technological methods for capturing and comparing patterns of skin creases across the knuckles. Knuckle creases are universally accepted as a biometric with high levels of discrimination (Choraś & Kozik, 2010; Jaswal, Nigam & Nath 2017; Kumar & Xu 2014; Neware, Mehta & Zadgaonkar 2012; 2014).…”

Section: Underpinning Researchmentioning

confidence: 99%

Forensic examination of the hand

Hackman

Black

2023

Royal Anthropological Inst

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Using anatomical feature comparison to consider potential matches from hand images to identify or eliminate suspects in an investigation, commonly relating to those involved in the production of images of child sexual abuse, is an emerging forensic methodology that has become increasingly utilized by police forces within the United Kingdom. This article gives an overview of the development of a process that is based on an understanding of anatomical variation and shows how new approaches can be introduced to forensic investigations. It describes the rationale that led to the development of the investigative methodology within the United Kingdom, and how it is implemented to ensure that comparisons and the subsequent reports are both methodologically sound and admissible in any of the courts within the UK legal jurisdictions.

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Section: Underpinning Researchmentioning

confidence: 99%

Forensic examination of the hand

Hackman

Black

2023

Royal Anthropological Inst

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“…The paper features the outcomes of tests completed on two unique bases of blood vessels in a hand. Neware et al (2014) introduced a component-based identification strategy for developing a biometric identifier called FKP. Strategies were utilised for feature-based methodology utilising Gabor filter strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature level fusion using physical biometric traits

Carol

Fred²

2018

IJBET

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Biometric is a measuring technique that is utilised to discern a person's identity through a physiological or behavioural feature. Each modality on its own cannot always be reliable for recognition. The multimodal biometric system offers several advantages over the traditional biometric system. In order to overcome the limitations of the unimodal biometrics, the fusion of multimodal biometric traits have been used. In this paper, a multimodal biometric based on fingerprint, palm print and knuckle print is presented. Gabor filter is employed to extract features from these biometric traits. The features extracted from these biometric traits are normalised and fused together. The features are fused by feature level fusion, and the result of the proposed method provides efficient authentication, by minimising the FAR by 1.43%, and the framework achieves satisfactory performance.

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“…Automated personal identification and authentication systems have an essential concern for both academic research and industries because of their wide range of uses, which include anything from low enforcement security to e-commerce (Neware et al, 2014). In the past 10 years, biometrics has attracted a lot of attention because of its many applications in various fields.…”

Section: Introductionmentioning

confidence: 99%

“…This is an open access under a CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/) for a long time. But this cannot be used mainly because it is a very expensive system compared to other biometric systems (Neware et al, 2014). Hand traits biometric systems, for example, Fingerprints and finger knuckle prints, have recently become an active research frontier because of their varied characteristics for instance (i) it's simple to capture with low-cost equipment, (ii) hand traits contain extremely distinctive characteristics that allow personal identification, (iii) because they were collected in a contactless way and provided excellent quality in terms of both accuracy and speed, hand characteristics had a high user acceptance rate (Usha & Ezhilarasan, 2018).…”

Section: Introductionmentioning

confidence: 99%

Deep ContactlessS 3D-Middle Finger Knuckle Recognition

Surameery

2021

SJUOZ

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Automation security is one of the main concerns of modern times. A safe and reliable identity verification system is in great demand. A biometric verification system can represent a reliable method of identifying an individual. The knuckle pattern is considered to be one of the emerging hand biometrics because it has the potential to identify individuals. This presented work explores the possibility of using 3D middle finger knuckle for biometric identification. The study provides a new simple, trained from scratch but effective deep convolutional neural network model that designed for 3D figure knuckle print recognition. To the best of the author's knowledge, this is the first successful attempt to develop 3D middle finger knuckle based authentication system using Convolutional Neural Network (CNN). Extensive experiment was carried out using (HKPolyU) the 3D knuckle image database of Hong Kong Polytechnic University. The performance of the proposed CNN model has been evaluated using two session data obtained from different camera lenses. The model designed to be implemented in a real-time system. It can be deployed in a small-scale environment like offices, houses, or personal devices, where the training is very easy. The experimental results were very encouraging and showed the potential of using the 3D middle finger knuckle pattern for biometric applications. The results confirmed that despite various challenges compared with other studies in the same field, the proposed method provides an optimistic solution with an accuracy of 71%.

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Finger Knuckle Print Identification using Gabor Features

Cited by 8 publications

References 21 publications

Forensic examination of the hand

Forensic examination of the hand

Feature level fusion using physical biometric traits

Deep ContactlessS 3D-Middle Finger Knuckle Recognition

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