Abstract
Several devices are capable of capturing images with a large number of people, including those of high resolution known as gigapixel images. These images can be helpful for studies and investigations, such as finding people in a crowd. Although they can provide more details, the task of identifying someone in the crowd is quite challenging and complex. In this paper, we aim to assist the work of a human observer with larger images with crowds by reducing the search space for several images to a ranking of ten images related to a specific person. Our model collects faces in a crowded gigapixel image and then searches for people using three different poses (front, right and left). We built a handcraft dataset with 42 people to evaluate our method, achieving a recognition rate of 69% in the complete dataset. We highlight that, from the 31% “not found” among the top ten in the ranking, many of them are very close to this boundary and, in addition, 92% of non-matched are occluded by some accessory or another face. Experimental results showed great potential for our method to support a human observer to find people in the crowd, especially cluttered images, providing her/him with a reduced search space.
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Notes
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Available at https://edition.cnn.com/interactive/2017/01/politics/trump-inauguration-gigapixel. (Access was unstable when finishing this text).
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Acknowledgment
The authors thank CAPES-Brazil (Coordination for the Improvement of Higher Education Personnel) for this study’s partial support.
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Ferreira, C.B.R. et al. (2020). Where’s Wally: A Gigapixel Image Study for Face Recognition in Crowds. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_30
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DOI: https://doi.org/10.1007/978-3-030-64559-5_30
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