PROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control

Sequeira, Ana F.; Chen, Lulu; Ferryman, James; Galdi, Chiara; Chiesa, Valeria; Dugelay, Jean‐Luc; Maik, Patryk; Gmitrowicz, Piotr; Szklarski, Łukasz; Prommegger, Bernhard; Kauba, Christof; Kirchgasser, Simon; Uhl, Andreas; Grudzie, Artur; Kowalski, Marcin

doi:10.23919/biosig.2018.8552926

Cited by 19 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The database prepared for the research consisted of 132 subjects selected from three databases: In-House, CARL [ 28 ], and PROTECT [ 29 ]. The characteristics of each database are presented below.…”

Section: Methodsmentioning

confidence: 99%

Thermal Face Verification through Identification

Grudzień

Kowalski

Pałka

2021

Sensors

Self Cite

View full text Add to dashboard Cite

This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

show abstract

Section: Methodsmentioning

confidence: 99%

Thermal Face Verification through Identification

Grudzień

Kowalski

Pałka

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the purpose of algorithm development, two datasets [ 25 , 27 ] of thermal images presenting genuine subjects were used. In order to validate the method against changing subject’s state, images of subjects after physical effort were added.…”

Section: Pad Methodsmentioning

confidence: 99%

A Study on Presentation Attack Detection in Thermal Infrared

Kowalski

2020

Sensors

Self Cite

View full text Add to dashboard Cite

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

show abstract

“…A first version of the PROTECT data set was collected in 2017 and the results of the experiments carried on it are reported in [5].…”

Section: Protect Multimodal Data Set Versionmentioning

confidence: 99%

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

et al. 2020

Self Cite

View full text Add to dashboard Cite

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this paper illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

show abstract

PROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control

Cited by 19 publications

References 5 publications

Thermal Face Verification through Identification

Thermal Face Verification through Identification

A Study on Presentation Attack Detection in Thermal Infrared

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

Contact Info

Product

Resources

About