Face Recognition Breakthrough

By Kirk L. Kroeker

By using sparse representation and compressed sensing, researchers have been able to demonstrate significant improvements in accuracy over traditional face-recognition techniques.

The theories of sparse representation and compressed sensing have emerged in recent years as powerful methods for efficiently processing data in unorthodox ways. One of the areas where these theories are having a major impact today is in computer vision. In particular, the theories have given new life to the field of face recognition, which has seen only incremental increases in accuracy and efficiency in the past few decades. Now, thanks to the application of these theories to classic face-recognition problems, researchers at the University of Illinois at Urbana-Champaign (UIUC) have been able to demonstrate significant improvements in accuracy over traditional techniques.

(This article appeared in CACM, vol. 52, no. 9, Aug. 2009, pp. 18-19.)

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