Interpretable Local Frequency Binary Pattern (LFrBP) based Joint Continual Learning Network for Heterogeneous Face Recognition

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpretable Local Frequency Binary Pattern (LFrBP) based Joint Continual Learning Network for Heterogeneous Face Recognition

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Interpretable Local Frequency Binary Pattern (LFrBP) based Joint Continual Learning Network for Heterogeneous Face Recognition

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Information Forensics and Security

ISSN

1556-6013

e-ISSN

1556-6021

Svazek periodika

17

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2125-2136

Kód UT WoS článku

000812529100006

EID výsledku v databázi Scopus

2-s2.0-85131733025