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Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild

Mohamed Khamis, Anita Baier, Niels Henze, Florian Alt, Andreas Bulling

Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 280:1-280:12, 2018.


Abstract

Commodity mobile devices are now equipped with high-resolution front-facing cameras, paving the way for applications in biometrics, facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos taken from the front-facing camera of smartphones and associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. We further identified an influence of users’ current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that state-of-the-art face detection algorithms perform poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art’s limitations.

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BibTeX

@inproceedings{khamis18_chi, title = {Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild}, author = {Khamis, Mohamed and Baier, Anita and Henze, Niels and Alt, Florian and Bulling, Andreas}, year = {2018}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, doi = {10.1145/3173574.3173854}, pages = {280:1-280:12} }