[visionlist] CFP: Deep Learning for Facial Informatics (Special Issue on), deadline: 15.07.2018

Radu Timofte timofte.radu at gmail.com
Mon Jun 25 07:44:01 -05 2018

Apologies for cross-posting


2018 MDPI Symmetry Special Issue on Deep Learning for Facial Informatics


Gee-Sern Jison Hsu (jison at mail.ntust.edu.tw)
Radu Timofte (radu.timofte at vision.ee.ethz.ch)


Deep learning has been revolutionizing many fields in computer vision, and
facial informatics is one of the major fields. Novel approaches and
performance breakthroughs are often reported on existing benchmarks. As the
performances on existing benchmarks are close to saturation, larger and
more challenging databases are being made and considered as new benchmarks,
further pushing the advancement of the technologies. Considering face
recognition, for example, the DeepFace and DeepID report nearly perfect and
better-than-human performances on the LFW (Labeled Faces in the Wild)
benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A
(IJB-A) and MegaFace, are accepted as new standards for evaluating the
performance of a new approach. Such an evolution is also seen in other
branches of face informatics.

This special issue aims to delineate the state-of-the-art technologies in
deep learning for facial informatics from multiple perspectives, including
methods, architectures, databases, protocols and applications. Researchers
from both academia and industry are cordially invited to share their latest
advancements on all aspects of facial informatics. Both image- and
video-based works are welcome. Topics for this special issue include, but
are not limited to, the following: face recognition, face alignment, face
detection and tracking, facial attributes (e.g., age, expression, gender,
ethnicity, micro-expression, …), face hallucination, facial trait analysis.

All received submissions will be subject to peer review by experts in the
field and will be evaluated based on their relevance to this special issue,
level of novelty, significance of contribution, and the overall quality.


Manuscripts should be submitted online at www.mdpi.com by registering and
logging in to this website. Once you are registered, the submission form is
available at: http://www.mdpi.com/user/manuscripts/upload/?journal=symmetry.
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: http://www.mdpi.com/journal/symmetry/instructions .
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). This charge will be waived
for the top ranked 5 accepted submissions (therefore, no fee for the best


Submission: July 15, 2018  (extended!)
First Review: Aug 15, 2018
First Revision Due: Sep. 15, 2018
Final Review: Sep. 25, 2018
Final Revision Due: Oct. 5, 2018
Acceptance Notification: Nov. 15, 2018
Online Publication: Right after acceptance

Gee-Sern Jison Hsu (jison at mail.ntust.edu.tw)
Radu Timofte (radu.timofte at vision.ee.ethz.ch)

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20180625/ee11a164/attachment.html>

More information about the visionlist mailing list