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Thermal and Cross-spectral Palm Image Matching in the Visual Domain by Robust Image Transformation

 
: Bartuzi, Ewelina; Damer, Naser

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; International Association for Pattern Recognition -IAPR-:
12th IAPR International Conference on Biometrics, ICB 2019 : 4-7 June 2019, Crete, Greece
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-3640-0
ISBN: 978-1-7281-3641-7
S.305-312
International Conference on Biometrics (ICB) <12, 2019, Crete>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
CRISP; Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); biometrics; Biometric features; biometric sensors

Abstract
Synthesizing visual-like images from those captured in the thermal spectrum allows for direct cross-domain comparisons. Moreover, it enables thermal-to-thermal comparisons that take advantage of feature extraction methodologies developed for the visual domain. Hand based biometrics are socially accepted and can operate in a touchless mode. However, certain deployment scenarios requires captures in non-visual spectrums due to impractical illumination requirements. Generating visual-like palm images from thermal ones faces challenges related to the nature of hand biometrics. Such challenges are the dynamic nature of the hand and the difficulties in accurately aligning hand’s scale and rotation, especially in the understudied thermal domain. Building such a synthetic solution is also challenged by the lack of large-scale databases that contain images collected in both spectra, as well as generating images of appropriate resolutions. Driven by these challenges, this paper presents a novel solution to transfer thermal palm images into high-quality visual-like images, regardless of the limited training data, or scale and rotational variations. We proved quality similarity and high correlation of the generated images to the original visual images. We used the synthesized images within verification approaches based on CNN and hand crafted-features. This allowed significantly improved the cross-spectral and thermal-to-thermal verification performances, reducing the EER from 37.12% to 16.25% and from 3.04% to 1.65%, respectively in both cases when using CNN-based features.

: http://publica.fraunhofer.de/dokumente/N-599596.html