Biometrics at a distance (e.g., gait recognition, person 
re-identification, etc.) is a particular case of biometric analysis 
that usually does not require the conscious participation of the 
target subject, being non-invasive at the same time. However, the 
sample acquisition is almost always affected by adverse conditions, 
e.g., the lack of details due to the distance itself, so that the 
robustness to distortions of adopted biometric methods is of paramount 
importance. This is a well-established topic in the field of 
information forensics and security. With the arrival of the Deep 
Learning era, new approaches have started to emerge in dealing with 
this task. However, in contrast to other computer vision and machine 
learning problems, as general image/video classification, one of the 
main challenges that has to be addressed in this type of biometric 
problem, amongst others, is the lack or limited amount of available 
annotated data sets for effectively training deep models.
The aim of this special issue is to gather and promote novel 
deep-learning based approaches for addressing the task of biometrics 
at a distance. Specifically, we are interested in works that propose 
new methods to improve the recognition accuracy, the computational 
burden and/or the scalability of the domain of application for 
biometrics, being the application of the deep learning paradigm the 
main component. Special attention will be paid to privacy protection 
and data security in the context of biometrics. In addition, new large 
realistic annotated datasets for the related tasks are welcome.

Topics
================
The topics of interest for this special issue include, but are not 
limited to, the following ones:
*         Gait recognition with Deep Learning
*         Face recognition (low resolution) at a distance with Deep Learning
*         Person re-identification with Deep Learning
*         Soft biometrics at a distance with Deep Learning
*         Multimodal biometrics at a distance with Deep Learning
*         Heterogeneous and cross-modal biometrics at a distance with 
Deep Learning
*         Information fusion for biometrics with Deep Learning
*         Incremental learning for biometrics at a distance with Deep Learning
*         Semi- and weakly-supervised learning for biometrics at a 
distance with Deep Learning
*         Algorithms for effective transfer learning applied to 
biometrics at at distance
*         Multi-task learning applied to biometrics at at distance
*         Privacy protection and data security applied to Biometrics 
at a distance
*         Processing and enhancement of low-quality biometric data

Important Dates
================
* Submissions due    31/July/2022
* First Review due    30/September/2022
* Revised manuscript due    30/November/2022
* Second review due    15/January/2023
* Final manuscript due:    28/February/2023

Guest Editors
================
Manuel J. Marin-Jimenez (Lead GE), University of Cordoba, Spain. 
Email: mjmarinATuco.es
Shiqi Yu, SUSTech, China. Email: yusqATsustech.edu.cn
Yasushi Makihara, Osaka University, Japan. Email: 
makiharaATam.sanken.osaka-u.ac.jp
Vishal Patel, Johns Hopkins University, USA. Email: vpatel36ATjhu.edu
Maria de Marsico, Sapienza Università di Roma, Italy. Email: 
demarsicoATdi.uniroma1.it
Maneet Singh, AI Garage-Mastercard, India. Email: maneetsATiiitd.ac.in