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Deep Learning-Based Biometric Recognition
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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: closed (31 December 2021) | Viewed by 20676

Special Issue Editor

Prof. Dr. Shi-Jinn Horng

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10672, Taiwan
Interests: deep learning and big data; biometric recognition; information security; cloud and fault computing; multimedia applications; medical applications
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

  • Deep learning
  • Biometric recognitions
  • Biometric applications

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Published Papers (4 papers)

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Research

14 pages, 28403 KiB  
Article
Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network
by Haopeng Wu, Zhiying Lu, Jianfeng Zhang, Xin Li, Mingyue Zhao and Xudong Ding
Appl. Sci. 2021, 11(4), 1428; https://doi.org/10.3390/app11041428 - 4 Feb 2021
Cited by 14 | Viewed by 2099
Abstract
This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative [...] Read more.
This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Recognition)
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15 pages, 1594 KiB  
Article
Identification of Epileptic EEG Signals Using Convolutional Neural Networks
by Rahib Abiyev, Murat Arslan, John Bush Idoko, Boran Sekeroglu and Ahmet Ilhan
Appl. Sci. 2020, 10(12), 4089; https://doi.org/10.3390/app10124089 - 13 Jun 2020
Cited by 66 | Viewed by 7487
Abstract
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain [...] Read more.
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Recognition)
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20 pages, 2100 KiB  
Article
Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
by Philippe Terrier
Appl. Sci. 2020, 10(3), 774; https://doi.org/10.3390/app10030774 - 22 Jan 2020
Cited by 34 | Viewed by 5512
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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12 pages, 2884 KiB  
Article
Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
by Ali Ismail Awad and M. Hassaballah
Appl. Sci. 2019, 9(22), 4914; https://doi.org/10.3390/app9224914 - 15 Nov 2019
Cited by 21 | Viewed by 4657
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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