Mallat, Khawla ; Damer, Naser ; Boutros, Fadi ; Kuijper, Arjan ; Dugelay, Jean-Luc (2019)
Cross-spectrum thermal to visible face recognition based on cascaded image synthesis.
2019 International Conference on Biometrics (ICB). Crete, Greece (04.06.2019-07.06.2019)
doi: 10.1109/ICB45273.2019.8987347
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Face synthesis from thermal to visible spectrum is fundamental to perform cross-spectrum face recognition as it simplifies its integration in existing commercial face recognition systems and enables manual face verification. In this paper, a new solution based on cascaded refinement networks is proposed. This method generates visible-like colored images of high visual quality without requiring large amounts of training data. By employing a contextual loss function during training, the proposed network is inherently scale and rotation invariant. We discuss the visual perception of the generated visible-like faces in comparison with recent works. We also provide an objective evaluation in terms of cross-spectrum face recognition, where the generated faces were compared against a gallery in visible spectrum using two state-of-the-art deep learning based face recognition systems. When compared to the recently published TV-GAN solution, the performance of the face recognition systems, OpenFace and LightCNN, was improved by a 42.48% (i.e. from 10.76% to 15.37%) and a 71.43% (i.e. from 33.606% to 57.612%), respectively.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Mallat, Khawla ; Damer, Naser ; Boutros, Fadi ; Kuijper, Arjan ; Dugelay, Jean-Luc |
| Art des Eintrags: | Bibliographie |
| Titel: | Cross-spectrum thermal to visible face recognition based on cascaded image synthesis |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Veranstaltungstitel: | 2019 International Conference on Biometrics (ICB) |
| Veranstaltungsort: | Crete, Greece |
| Veranstaltungsdatum: | 04.06.2019-07.06.2019 |
| DOI: | 10.1109/ICB45273.2019.8987347 |
| URL / URN: | https://ieeexplore.ieee.org/document/8987347 |
| Kurzbeschreibung (Abstract): | Face synthesis from thermal to visible spectrum is fundamental to perform cross-spectrum face recognition as it simplifies its integration in existing commercial face recognition systems and enables manual face verification. In this paper, a new solution based on cascaded refinement networks is proposed. This method generates visible-like colored images of high visual quality without requiring large amounts of training data. By employing a contextual loss function during training, the proposed network is inherently scale and rotation invariant. We discuss the visual perception of the generated visible-like faces in comparison with recent works. We also provide an objective evaluation in terms of cross-spectrum face recognition, where the generated faces were compared against a gallery in visible spectrum using two state-of-the-art deep learning based face recognition systems. When compared to the recently published TV-GAN solution, the performance of the face recognition systems, OpenFace and LightCNN, was improved by a 42.48% (i.e. from 10.76% to 15.37%) and a 71.43% (i.e. from 33.606% to 57.612%), respectively. |
| Freie Schlagworte: | Biometrics Biometric identification systems Face recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 17 Apr 2020 10:16 |
| Letzte Änderung: | 17 Apr 2020 10:16 |
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