Together with B. Vijaya Kumar, a professor of electrical and computer engineering at Carnegie Mellon, and Simon Baker of Microsoft Research, Hennings-Yeomans has tested an approach that improves upon face-recognition systems that use standard super-resolution. Instead of applying super-resolution algorithms to an image and running the results through a face-recognition system, the researchers designed software that combines aspects of a super-resolution algorithm and the feature-extraction algorithm of a face-recognition system. To find a match for an image, the system first feeds it through this intermediary algorithm, which doesn’t reconstruct an image that looks better to the human eye, as super-resolution algorithms do. Instead, it extracts features that are specifically readable by the face-recognition system. In this way, it avoids the distortions characteristic of super-resolution algorithms used alone.
In prior work, the researchers showed that the intermediary algorithm improved face-matching results when finding matches for a single picture. In a paper being presented at the IEEE International Conference on Biometrics: Theory, Systems, and Applications later this month, the researchers show that the system works even better, in some cases, when multiple images or frames, even from different cameras, are used.
The approach shows promise, says Pawan Sinha, a professor of brain and cognitive sciences at MIT. The problem of low-resolution images and video “is undoubtedly important and has not been adequately tackled by any of the commercial face-recognition systems that I know of,” he says. “Overall, I like the work.”
Ultimately, says Hennings-Yeomans, super-resolution algorithms still need to be improved, but he doesn’t think it would take too much work to apply his group’s approach to, say, a Web tool that searches YouTube videos. “You’re going to see face-recognition systems for image retrieval,” he says. “You’ll Google not by using text queries, but by giving an image.”