Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), 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 as well as 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. Furthermore, we 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 a state-of-the-art face detection algorithm performs 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

EyeSpot: leveraging gaze to protect private text content on mobile devices from shoulder surfing

As mobile devices allow access to an increasing amount of private data, using them in public can potentially leak sensitive information through shoulder surfing. This includes personal private data (e.g., in chat conversations) and business-related content (e.g., in emails). Leaking the former might infringe on users’ privacy, while leaking the latter is considered a breach of the EU’s General Data Protection Regulation as of May 2018. This creates a need for systems that protect sensitive data in public. We introduce EyeSpot, a technique that displays content through a spot that follows the user’s gaze while hiding the rest of the screen from an observer’s view through overlaid masks. We explore different configurations for EyeSpot in a user study in terms of users’ reading speed, text comprehension, and perceived workload. While our system is a proof of concept, we identify crystallized masks as a promising design candidate for further evaluation with regard to the security of the system in a shoulder surfing scenario

TransparentHMD: Revealing the HMD User's Face to Bystanders

While the eyes are very important in human communication, once a user puts on a head mounted display (HMD), the face is obscured from the outside world's perspective. This leads to communication problems when bystanders approach or collaborate with an HMD user. We introduce transparentHMD, which employs a head-coupled perspective technique to produce an illusion of a transparent HMD to bystanders. We created a self contained system, based on a mobile device mounted on the HMD with the screen facing bystanders. By tracking the relative position of the bystander using the smartphone's camera, we render an adapting perspective view in realtime that creates the illusion of a transparent HMD. By revealing the user's face to bystanders, our easy to implement system allows for opportunities to investigate a plethora of research questions particularly related to collaborative VR systems