Special Issue "Deep Learning-Based Biometric Recognition"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editor

Prof. Shi-Jinn Horng
E-Mail Website
Guest Editor
National Taiwan University of Science and Technology, Taiwan
Interests: Deep Learning & Big Data; Biometric Recognition; Information Security; Cloud & Fault Computing; Multimedia Applications; Medical Applications

Special Issue Information

Dear Colleagues,

Deep learning is one of machine learning methods based on artificial neural networks. During the past few years, with the advances in deep learning, many new computation models have been proposed and significantly applied in speech processing, language processing, computer vision, information security, etc.

Biometric authentication is part of a broader family of information security. Biometrics authentication is used in computer science as a form of identification and access control. Biometric recognitions include both physiological and behavioral characteristics. The former is related to the shape of the body, including fingerprint, veins, face, palm print, DNA, hand geometry, iris, retina, EEG, heart beat and odour/scent. The latter is related to the pattern of behavior of a person, including typing rhythm, gait, and voice.

The recognition rates of the biometric identifiers are usually decreased in the different environments using the heuristic approaches but they can be improved through deep learning.

The goal of this special issue is to bring together a diverse but complementary set of contributions on emerging deep learning methods for problems in biometrics. The topics of this special issue include but not limit to the following:

  • Big data and large scale methods for biometrics
  • Biometrics in fingerprint, veins, face, DNA, palm print, hand geometry, iris, retina, EEG, heart beat and odour/scent
  • Biometrics in typing rhythm, gait, and voice
  • Biometrics in medical applications

Prof. Shi-Jinn Horng
Guest Editor

Manuscript Submission Information

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Keywords

  • Deep learning
  • Biometric recognitions
  • Biometric applications

Published Papers (2 papers)

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Research

Open AccessArticle
Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
Appl. Sci. 2020, 10(3), 774; https://doi.org/10.3390/app10030774 - 22 Jan 2020
Abstract
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure (COP) trajectory is sufficiently unique to identify a person with high certainty. Thirty-six [...] Read more.
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure (COP) trajectory is sufficiently unique to identify a person with high certainty. Thirty-six adults walked for 30 min on a treadmill equipped with a force platform that continuously recorded the positions of the COP. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2250 segments with an overall accuracy of 99.9%. A second set of 4500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used to fine tune the pretrained CNNs. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures while walking and that CNNs can learn the distinctive features of these trajectories. By applying a pretrained CNN (transfer learning), a couple of strides seem enough to learn and identify new gaits. However, these promising results should be confirmed in a larger sample under realistic conditions. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Recognition)
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Open AccessArticle
Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
Appl. Sci. 2019, 9(22), 4914; https://doi.org/10.3390/app9224914 - 15 Nov 2019
Abstract
Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an [...] Read more.
Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Recognition)
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