Interpretable Local Frequency Binary Pattern (LFrBP) based Joint Continual Learning Network for Heterogeneous Face Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019219" target="_blank" >RIV/62690094:18450/22:50019219 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9786806" target="_blank" >https://ieeexplore.ieee.org/document/9786806</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TIFS.2022.3179951" target="_blank" >10.1109/TIFS.2022.3179951</a>
Alternative languages
Result language
angličtina
Original language name
Interpretable Local Frequency Binary Pattern (LFrBP) based Joint Continual Learning Network for Heterogeneous Face Recognition
Original language description
Heterogeneous Face Recognition (HFR) is a challenging task due to the significant intra-class variation between the query and gallery images. The reason behind this vast intra-class variation is the varying image capturing sensors and the varying image representation techniques. Visual, Infrared, thermal images are the output of different sensors and viewed sketches, and composite sketches are the output of different image representation techniques. Conventional deep learning models are trying to solve the problem. Still, progress is impeded due to small HFR data samples, task-specific models (one model trained for face sketch-photo matching can’t perform well for NIR-VIS face matching), joint learning of two different HFR scenarios are not possible by one single deep network, and models are not interpretable. In this paper, to solve these major problems, we presented a novel interpretable Local Frequency Binary Pattern (LFrBP) based continual learning shallow network for HFR. The model is divided into two parts. A modality-invariant CNN model using the LFrBP feature, fine-tuned with CNN, is presented in the first part. The second part is based on continual learning to jointly learn the two HFR scenarios (face sketch-photo and NIR-VIS face matching) using a single network. Recognition results on different challenging HFR databases depict the superiority of the proposed model over other state-of-the-art deep learning-based methods. IEEE
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Information Forensics and Security
ISSN
1556-6013
e-ISSN
1556-6021
Volume of the periodical
17
Issue of the periodical within the volume
June
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
2125-2136
UT code for WoS article
000812529100006
EID of the result in the Scopus database
2-s2.0-85131733025