Abstract
Performing covert biometric recognition in surveillance environments has been regarded as a grand challenge, considering the adversity of the conditions where recognition should be carried out (e.g., poor resolution, bad lighting, off-pose and partially occluded data). This special issue compiles a group of approaches to this problem.
| Original language | English |
|---|---|
| Article number | 8423530 |
| Pages (from-to) | 41-67 |
| Number of pages | 27 |
| Journal | IEEE Intelligent Systems |
| Volume | 33 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 May 2018 |
Bibliographical note
Publisher Copyright:© 2001-2011 IEEE.
Funding
This work is supported by ‘’FCT – Fundação para a Ciência e Tecnologia” (Portugal), through the project “UID/EEA/50008/2013”. This work has been partially supported by project CogniMetrics TEC2015-70627-R (MINECO/FEDER). E. GonzalezSosa is supported by a PhD scholarship from Universidad Au-tonoma de Madrid. This work was supported by TUBITAK project number 113E067 and by a Marie Curie FP7 Integration Grant within the 7th EU Framework Programme.
| Funders | Funder number |
|---|---|
| 7th EU Framework Programme | |
| MINECO/FEDER | |
| TUBITAK | 113E067 |
| Fundo Regional para a Ciência e Tecnologia | UID/EEA/50008/2013 |
| Marie Curie | |
| Fundação para a Ciência e a Tecnologia | |
| Universidad Autónoma de Madrid |
Keywords
- QUIS-CAMPI
- deep models
- face recognition
- surveillance