Abstract
The ever growing field of face recognition is constantly expanding to tackle new and more challenging, problems as the advances in algorithms yield higher accuracy results. The most recent advances have opened up the possibility of conducting high accuracy face recognition on faces from completely uncontrolled sources, such as search engines, social-media, and other online sources. Conducting face recognition in this area is usually deemed as faces-in-the-wild, given the unbounded nature in which faces are collected. While performing face recognition on faces-in-the-wild datasets has many advantages, it can make it difficult to determine the limitations of the face recognition algorithm in terms of the scenarios in which the faces were collected. In this work, we will collect a simulated faces-in-the-wild dataset using four cell phones (common sources for faces-in-the-wild) in varying scenarios (distance, lighting, background, etc.) to fully demonstrate the capability of newly proposed deep learning based methods of face recognition. Furthermore, we will contrast this with previous, standard, methods of face recognition in the same scenarios to see how recent improvements in the filed have opened up new capabilities.
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OpenCV: https://opencv.org/
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Acknowledgements
This research was supported in part by the Department of Homeland Security (DHS) and was conducted with the assistance of Dr. Neeru Narang.
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Martin, M., Bourlai, T. (2020). Unconstrained Face Recognition Using Cell Phone Devices: Faces in the Wild. In: Bourlai, T., Karampelas, P., Patel, V.M. (eds) Securing Social Identity in Mobile Platforms. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-39489-9_7
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