Determining What Video Conference Participants Are Typing from Watching Shoulder Movements
Accuracy isn’t great, but that it can be done at all is impressive.
Murtuza Jadiwala, a computer science professor heading the research project, said his team was able to identify the contents of texts by examining body movement of the participants. Specifically, they focused on the movement of their shoulders and arms to extrapolate the actions of their fingers as they typed.
Given the widespread use of high-resolution web cams during conference calls, Jadiwala was able to record and analyze slight pixel shifts around users’ shoulders to determine if they were moving left or right, forward or backward. He then created a software program that linked the movements to a list of commonly used words. He says the “text inference framework that uses the keystrokes detected from the video … predict[s] words that were most likely typed by the target user. We then comprehensively evaluate[d] both the keystroke/typing detection and text inference frameworks using data collected from a large number of participants.”
In a controlled setting, with specific chairs, keyboards and webcam, Jadiwala said he achieved an accuracy rate of 75 percent. However, in uncontrolled environments, accuracy dropped to only one out of every five words being correctly identified.
Other factors contribute to lower accuracy levels, he said, including whether long sleeve or short sleeve shirts were worn, and the length of a user’s hair. With long hair obstructing a clear view of the shoulders, accuracy plummeted.
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Clive Robinson • November 4, 2020 12:53 PM
In a way, this is an extension of earlier techniques that only had coarse almost blobby images of hand movments.
Such as those from “through the wall microwave imaging”… Or THz systems where clothing and long hair in effect become invisible.
Which raises the question of “Will other imaging, such as passive imaging from WiFi access points give sufficient information?”…