Towards a robust face recognition system using compressive sensing

An application of compressive sensing (CS) theory in imagebased robust face recognition is considered. Most contemporary face recognition systems suffer from limited abilities to handle image nuisances such as illumination, facial disguise, and pose misalignment. Motivated by CS, the problem has been recently cast in a sparse representation framework: The sparsest linear combination of a query image is sought using all prior training images as an overcomplete dictionary, and the dominant sparse coefficients reveal the identity of the query image. The ability to perform dense error correction directly in the image space also provides an intriguing solution to compensate pixel corruption and improve the recognition accuracy exceeding most existing solutions. Furthermore, a local iterative process can be applied to solve for an image transformation applied to the face region when the query image is misaligned. Finally, we discuss the state of the art in fast 1-minimization to improve the...

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Allen Y. Yang, Zihan Zhou, Yi Ma, Shankar Sastry
• Keywords: Face Recognition, Image, INTERSPEECH 2010, Robust Face Recognition, Signal Processing