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Symmetry
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Deep Learning-Based Biometric Technologies II
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Deep Learning-Based Biometric Technologies II

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 24986

Special Issue Editor

Prof. Dr. Kang Ryoung Park

E-Mail Website
Guest Editor
Division of Electronics and Electrical Engineering, Dongguk University, 30, Pildong- ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Interests: deep learning; biometrics; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments have led to the widespread use of biometric technologies, such as face, fingerprint, vein, iris, palmprint, wrinkle, voice, and gait recognition, in a variety of applications in access control, financial transactions on mobile devices, and automatic teller machines (ATMs). While existing biometric technology has matured, its performance is still affected by various environmental conditions, and recent approaches have been attempted to combine deep learning techniques with conventional biometrics to guarantee the higher performance. The objective of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in deep learning-based biometric technologies. We solicit the original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include, but are not limited to:

  •  Region of interest (ROI) or feature point detection for biometrics based on deep learning
  •  Biometric feature extraction based on deep learning
  •  Biometric recognition based on deep learning
  •  Soft biometrics based on deep learning
  •  Multimodal biometrics based on deep learning
  •  Spoof detection based on deep learning

Prof. Kang Ryoung Park
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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15 pages, 3947 KiB  
Article
Anthropometric Landmarks Extraction and Dimensions Measurement Based on ResNet
by Xun Wang, Baohua Liu, Yukun Dong, Shanchen Pang and Xixi Tao
Symmetry 2020, 12(12), 1997; https://doi.org/10.3390/sym12121997 - 03 Dec 2020
Cited by 5 | Viewed by 4411
Abstract
Anthropometric dimensions can be acquired in 2D images by landmarks. Body shape variance causes low accuracy and bad robustness of landmarks extracted, and it is difficult to determine the position of axis division point when dimensions are calculated by the ellipse model. In [...] Read more.
Anthropometric dimensions can be acquired in 2D images by landmarks. Body shape variance causes low accuracy and bad robustness of landmarks extracted, and it is difficult to determine the position of axis division point when dimensions are calculated by the ellipse model. In this paper, landmarks are extracted from images by convolutional neural network instead of the gradient of body outline. A general multi-ellipse model is proposed, the anthropometric dimensions are obtained from the length of different elliptical segments and the position of axis division point is determined by thickness–width ratio of body parts. Finally, an evaluation is completed based on 87 subjects, in which it turns out that the average accuracy of our method for identifying landmarks is 96.6%, when the number of rotation angles is 2, the three main dimensional errors calculated by our model are smaller than existing method, and the errors of other dimensions are also within the margin of error for garment measuring. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies II)
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17 pages, 5080 KiB  
Article
Fall Detection Based on Key Points of Human-Skeleton Using OpenPose
by Weiming Chen, Zijie Jiang, Hailin Guo and Xiaoyang Ni
Symmetry 2020, 12(5), 744; https://doi.org/10.3390/sym12050744 - 05 May 2020
Cited by 117 | Viewed by 14774
Abstract
According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it [...] Read more.
According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall.The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking, and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by OpenPose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to-height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies II)
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43 pages, 774 KiB  
Systematic Review
Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review
by Noel Zacarias-Morales, Pablo Pancardo, José Adán Hernández-Nolasco and Matias Garcia-Constantino
Symmetry 2021, 13(2), 214; https://doi.org/10.3390/sym13020214 - 28 Jan 2021
Cited by 13 | Viewed by 5201
Abstract
Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. [...] Read more.
Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined. Full article
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies II)
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