This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by ×2, ×3 and ×4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the ×2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.

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Face Recognition is among the most useful picture handling applications and plays a significant part in the specialized field. Recognition of the human face is a functioning issue for verification purposes explicitly with regards to participation of understudies. Participation framework utilizing face recognition is a method of perceiving understudies by utilizing face biostatistics dependent on the top quality observing and other PC advances. The advancement of this framework is intended to achieve digitization of the customary process for gauging participation by calling names and keeping up with pen-paper records. Current participation methodologies are drawn-out and tedious. Participation records can be handily controlled by manual recording. The customary course of making participation and present biometric frameworks are powerless against intermediaries. This paper is accordingly proposed to handle this multitude of issues.