Personal identification using Eigenfeet, Ballprint and Foot geometry biometrics

Uhl, Andreas; Wild, Peter

doi:10.1109/btas.2007.4401924

Cited by 13 publications

(19 citation statements)

References 14 publications

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“…-Shape: divides each finger into slices of equal height and keeps track of the local finger width for each slice (see [12] for an application on footprints).…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

Personal Recognition Using Single-Sensor Multimodal Hand Biometrics

Uhl

Wild

2008

Lecture Notes in Computer Science

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Abstract. Single-sensor approaches to multimodal biometric authentication targeting the human hand in multiple-matcher scenarios provide higher security in terms of accuracy and resistance to biometric system attacks than unimodal systems. This paper introduces a novel multimodal hand biometric system using palmar images acquired by a commercially available flatbed scanner. Hence, the presented approach to personal recognition is independent of specific biometric sensors, such as fingerprint readers or palmprint scanners. Experimental results with a minimum half total error rate of 0.003% using a database of 443 hand images will illustrate the performance improvement when hand-geometry, fingerprint and palmprint-based features are combined.

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“…-Shape: divides each finger into slices of equal height and keeps track of the local finger width for each slice (see [12] for an application on footprints).…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

Personal Recognition Using Single-Sensor Multimodal Hand Biometrics

Uhl

Wild

2008

Lecture Notes in Computer Science

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“…Multimodal biometric is another method of person identification [27,95,96]. [97] introduces a different classification of biometric.…”

Section: Discussionmentioning

confidence: 99%

“…Ref. [27][28][29] have implemented a system based on eigenfeet, ballprint and foot geometry for the small dataset of 16 entries. Ref.…”

Section: Motivationmentioning

confidence: 99%

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Cyber Forensic Review of Human Footprint and Gait-based System for Personal Identification in Crime Scene Investigation

Kk¹

2018

Preprint

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Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

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“…As research in biometric sector is attracting more attention in the past few years, people identification is becoming an essential component to numerous applications within our daily life. Therefore, There are certain types of biometrics based systems such as face fingerprint, hand geometry and iris gained more efforts of the researchers such as in [13] compare to less efforts to explore the utilization of footprint features in biometrics applications. The work reported in [14] presents a footprint personal recognition using scanning technology to recognize a person's identity based on computing details of features related to the height, weight and body mass index, and in addition to that, the researchers consider additional features such as foot length and foot category.…”

Section: Related Workmentioning

confidence: 99%

Deep transfer learning for human identification based on footprint: A comparative study

Abuqadumah¹,

Ali²,

Almisreb³

et al. 2019

PEN

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Identifying people based on their footprint has not yet gained enough attention from the researchers. Therefore, in this paper, an investigation of human identification conducted based on the footprint. Transfer Learning used as the main concept of this investigation. The aim of using Transfer Learning is to overcome the need for a large-scale dataset and achieve high accuracy with a low-scale dataset. Five well-known models used, namely, Alexnet, Vgg16, Vgg19, Googlenet, and Inception v3. Each of these models fine-tuned to fit-in the paper's topic. A dataset of 30 individuals constructed in order to train the models. The right and left footprint of each individual captured with iPhone camera. The models trained and evaluated based on the same settings. The evaluation shows that Inception v3 model achieved the highest accuracy compared to all other four models.

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Personal identification using Eigenfeet, Ballprint and Foot geometry biometrics

Cited by 13 publications

References 14 publications

Personal Recognition Using Single-Sensor Multimodal Hand Biometrics

Personal Recognition Using Single-Sensor Multimodal Hand Biometrics

Cyber Forensic Review of Human Footprint and Gait-based System for Personal Identification in Crime Scene Investigation

Deep transfer learning for human identification based on footprint: A comparative study

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