Abstract
It is possible for inexpensive cameras to include AI based features such as face recognition. However, a test framework for such cameras is required that will allow comparison of accuracy under differing conditions. This will then lead to the improvement of training data and algorithms.
A simple test framework has been developed and partially evaluated by testing multiple head/face accessories under different lighting conditions. Six participants took part and 300 pictures using a Huskylens were taken under a range of conditions. It was found that the camera could detect faces at a reasonable level of accuracy during ‘middle of the day’ lighting conditions, with or without head accessories. However, it delivers significantly lower detection rate with accessories that cover greater parts of the face and under green light.
There is still a need to further investigate this area of study with a higher number of participants in a more controlled environment. It is anticipated that better testing frameworks will lead to better algorithms, training data and specifications for users.
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Herdzik, A., James-Reynolds, C. (2022). Developing Testing Frameworks for AI Cameras. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XXXIX. SGAI-AI 2022. Lecture Notes in Computer Science(), vol 13652. Springer, Cham. https://doi.org/10.1007/978-3-031-21441-7_28
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DOI: https://doi.org/10.1007/978-3-031-21441-7_28
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