Special Issue "Biometric Recognition In-The-Wild"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editor

Guest Editor
Dr. Hugo Pedro Proença

Department of Computer Science, University of Beira Interior, 6201-001 Covilhã, Portugal
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Special Issue Information

Dear Colleagues,

The domain of Pattern Recognition has been attracting substantial research efforts, and is the scope of a large number of active research lines. Among these, biometrics is considered an especially successful case, even though performance is still strongly conditioned by the levels of cooperation demanded to subjects and by the environmental conditions during data-acquisition.

This Special Issue is particularly interested in emerging strategies to perform biometric recognition based on data acquired in wild scenarios, i.e., outdoors, from large distances and without requiring subjects cooperation for data acquisition, which should dramatically decrease the discriminability of the information obtained. The development of such types of recognition systems will broaden the range of domains where biometrics can be used, particularly for forensic/security purposes.

Considering that many problems remain to be solved, and that the proposal of techniques effective in such challenging situations requires vigorous research efforts, we are particularly interested in novel ideas about how to compensate for the adversity of the data/environments to perform accurate biometric recognition.

Topics of interest include, but are not strictly limited to:

  • Less controlled/covert data acquisition frameworks
  • Detection of regions of interest (landmarks) in real-world data
  • Segmentation of biometric traits
  • Robust feature encoding / matching
  • Multi-biometrics systems: fusion at different levels
  • Biometric recognition in surveillance scenarios
  • Announcement of challenging biometric data sets
  • Ethical issues/concerns about covert biometric recognition systems
  • Reaction plans against security incidents raised by automated recognition systems
  • Biometric recognition benchmarks for unconstrained data acquisition environments

Dr. Hugo Pedro Proença
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 papers will be 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 1200 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 (1 paper)

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Research

Open AccessArticle Age Estimation-Based Soft Biometrics Considering Optical Blurring Based on Symmetrical Sub-Blocks for MLBP
Symmetry 2015, 7(4), 1882-1913; https://doi.org/10.3390/sym7041882
Received: 17 June 2015 / Revised: 11 September 2015 / Accepted: 10 October 2015 / Published: 19 October 2015
Cited by 2 | PDF Full-text (29406 KB) | HTML Full-text | XML Full-text
Abstract
Because of its many useful applications, human age estimation has been considered in many previous studies as a soft biometrics. However, most existing methods of age estimation require a clear and focused facial image as input in order to obtain a trustworthy estimation
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Because of its many useful applications, human age estimation has been considered in many previous studies as a soft biometrics. However, most existing methods of age estimation require a clear and focused facial image as input in order to obtain a trustworthy estimation result; otherwise, the methods might produce increased estimation error when an image of poor quality is used as input. Image blurring is one of major factors that affect estimation accuracies because it can cause a face to appear younger (i.e., reduce the age feature in the face region). Therefore, we propose a new human age estimation method that is robust even with an image that has the optical blurring effect by using symmetrical focus mask and sub-blocks for multi-level local binary pattern (MLBP). Experiment results show that the proposed method can enhance age estimation accuracy compared with the conventional system, which does not consider the effects of blurring. Full article
(This article belongs to the Special Issue Biometric Recognition In-The-Wild)
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