Current Projects

Face Recognition from Video Key Words: Face recognition, eigen-face, eigen-face domain reconstruction, eigen-face domain recognition, independent component analysis, face super-resolution

Project Members

• Osman G. Sezer

Project Description

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.

Related Publications

• O. G. Sezer, Y. Altunbasak, A. Ercil, "Face Recognition with Independent Component Based Super-resolution", Proc. of SPIE Visual Communications and Image Processing Conference, VCIP – 2006, San Jose, CA.(invited paper), [pdf]
  "Best Student Paper Award"
• B. K. Gunturk, A. U. Batur, Y. Altunbasak, M. H. Hayes III, and R. M. Mersereau, “Eigenface-domain super-resolution for face recognition,” IEEE Transactions on Image Processing, May 2003 [pdf]
• B. Gunturk, A. Batur, Y. Altunbasak, M. H. Hayes III, and R. M. Mersereau, “Eigenface-based super-resolution for face recognition,” IEEE Int. Conf. on Image Processing, vol. 2, pp. 845-848, Rochester, NY, September 2002 [pdf]