Abstract
An application of compressive sensing (CS) theory in image-based 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 ℓ 1 -minimization to improve the speed of the robust face recognition system. The paper also provides useful guidelines to practitioners working in similar fields, such as acoustic/speech recognition.
| Original language | English (US) |
|---|---|
| Pages | 2250-2253 |
| Number of pages | 4 |
| State | Published - Dec 1 2010 |
| Event | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan Duration: Sep 26 2010 → Sep 30 2010 |
Conference
| Conference | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 |
|---|---|
| Country | Japan |
| City | Makuhari, Chiba |
| Period | 9/26/10 → 9/30/10 |
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All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Speech and Hearing
Cite this
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Towards a robust face recognition system using compressive sensing. / Yang, Allen Y.; Zhou, Zihan; Ma, Yi; Sastry, S. Shankar.
2010. 2250-2253 Paper presented at 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, Makuhari, Chiba, Japan.Research output: Contribution to conference › Paper
TY - CONF
T1 - Towards a robust face recognition system using compressive sensing
AU - Yang, Allen Y.
AU - Zhou, Zihan
AU - Ma, Yi
AU - Sastry, S. Shankar
PY - 2010/12/1
Y1 - 2010/12/1
N2 - An application of compressive sensing (CS) theory in image-based 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 ℓ 1 -minimization to improve the speed of the robust face recognition system. The paper also provides useful guidelines to practitioners working in similar fields, such as acoustic/speech recognition.
AB - An application of compressive sensing (CS) theory in image-based 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 ℓ 1 -minimization to improve the speed of the robust face recognition system. The paper also provides useful guidelines to practitioners working in similar fields, such as acoustic/speech recognition.
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M3 - Paper
SP - 2250
EP - 2253
ER -