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SI UER 2017 : IET Biometrics Special issue on Unconstrained Ear Recognition


When Jul 1, 2017 - Sep 1, 2017
Where Europe
Submission Deadline Sep 1, 2017
Notification Due Apr 3, 2017
Final Version Due May 1, 2017
Categories    biometrics   computer vision   ear recognition   special issue

Call For Papers


IET Biometrics Special Issue on

** Motivation **
Despite the numerous application possibilities in security, surveillance applications, forensics, criminal investigations or border control, the existing research in ear recognition has seldom gone beyond laboratory settings. This can mostly be attributed to the enormous appearance variability of ear images when captured in unconstrained settings. However, due to recent advances in computer vision, machine learning and artificial intelligence (e.g. with deep learning), many recognition problems are now solvable in unconstrained settings and many biometric modalities (including ear images) that were commonly too complex for real-life deployment are now becoming a viable source of data for identity recognition.

The goal of this special issue is to present the most advanced and up-to-date work related to unconstrained ear recognition, report recent findings and make fundamental and/or empirical contributions to the field. The special issue is meant to reflect the current state of technology in the area of ear recognition and serve as a reference for researchers working on problems relevant to ear-recognition technology.

** Topics of Interest **
We solicit original high-quality papers on various topics related to ear recognition in unconstrained settings. Authors of submitted papers are requested to clearly explain how their work contributes to the field. Topics of interest include, but are not limited to:
• Pre-processing techniques for ear recognition
• Normalization techniques for ear recognition
• Ear recognition in unconstrained settings
• Ear detection/segmentation/localization techniques
• Ear recognition with different modalities (2D, 3D, IR, NIS, ear-prints, heterogeneous)
• Machine learning techniques for ear recognition
• Elimination of influence of covariate factors
• Context-aware ear recognition and detection
• Fusion techniques involving ear images
• Individuality models/studies for ear recognition
• Scalability studies for ear recognition technology
• New datasets and performance evaluations
• Overviews and surveys related to ear recognition
• Related applications (e.g., in forensics).

All submissions will undergo a rigorous review process conducted by experts in the field.

** Important Dates **
Submission deadline: September 1st, 2017
Author notification: April 3rd, 2018
Target publication date: May, 2018

** Guest Editors **
Peter Peer, University of Ljubljana, Slovenia
Vitomir Štruc, University of Ljubljana, Slovenia

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