This novel video-WiFi cross-modal gait-based person identification system, which they refer to as XModal-ID (pronounced Cross-Modal-ID), could have a variety of applications, from surveillance and security to smart homes.
For instance, consider a scenario in which law enforcement has a video footage of a robbery. They suspect that the robber is hiding inside a house. The new technology could determine if the person inside is the same person in the video.
“Our proposed approach makes it possible to determine if the person behind the wall is the same as the one in video footage, using only a pair of off-the-shelf WiFi transceivers outside,” says Mostofi. “This approach utilizes only received power measurements of a WiFi link. It does not need any prior WiFi or video training data of the person to be identified. It also does not need any knowledge of the operation area.”
In the team’s experiments, one WiFi transmitter and one WiFi receiver are behind walls, outside a room where a person is walking. The transmitter sends a wireless signal whose power the receiver measures. Then, given video footage of a person from another area—and by using only such received wireless power measurements—the receiver can determine whether the person behind the wall is the same person seen in the video footage.
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This innovation builds on previous work in the Mostofi Lab, which has pioneered sensing with everyday radio frequency signals such as WiFi since 2009.
“However, identifying a person through walls, from candidate video footage, is a considerably challenging problem,” says Mostofi. Her lab’s success in this endeavor is due to the new prop ..
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