Thermal face recognition based on transformation by residual U-net and pixel shuffle upsampling
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11961 LNCS, Page: 679-689
2020
- 4Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
We present a thermal face recognition system that first transforms the given face in the thermal spectrum into the visible spectrum, and then recognizes the transformed face by matching it with the face gallery. To achieve high-fidelity transformation, the U-Net structure with a residual network backbone is developed for generating visible face images from thermal face images. Our work mainly improves upon previous works on the Nagoya University thermal face dataset. In the evaluation, we show that the rank-1 recognition accuracy can be improved by more than 10%. The improvement on visual quality of transformed faces is also measured in terms of PSNR (with 0.36 dB improvement) and SSIM (with 0.07 improvement).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85078564884&origin=inward; http://dx.doi.org/10.1007/978-3-030-37731-1_55; http://link.springer.com/10.1007/978-3-030-37731-1_55; http://link.springer.com/content/pdf/10.1007/978-3-030-37731-1_55; https://dx.doi.org/10.1007/978-3-030-37731-1_55; https://link.springer.com/chapter/10.1007/978-3-030-37731-1_55
Springer Science and Business Media LLC
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