Du Xing, Zhang Rongqing. Fusing color and texture features for blurred face recognition[J]. Infrared and Laser Engineering, 2014, 43(12): 4192-4197.
| Citation:
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Du Xing, Zhang Rongqing. Fusing color and texture features for blurred face recognition[J]. Infrared and Laser Engineering, 2014, 43(12): 4192-4197.
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Fusing color and texture features for blurred face recognition
- 1.
College of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China;
- 2.
Chongqing Bashu Secondary School,Chongqing 400013,China
- Received Date: 2014-04-09
- Rev Recd Date:
2014-05-10
- Publish Date:
2014-12-25
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Abstract
The texture feature based methods are widely used for face recognition. However, as the texture feature relies on the high frequency details of the image, the performance of a method that merely utilizes texture feature deteriorates drastically when the image is blurred. To overcome this defect of the texture feature, a method fusing color and texture features was proposed for blurred face recognition. This method extracted a type of facial color feature using an opponent color model which was in accordance with the human visual mechanism. This type of color feature and a certain type of texture feature were used for recognition separately, and then their similarity scores were fused to make final decision. The color feature is a description of the low frequency components of the image, which is robust to image blur and complementary to the texture feature. Experiments on the FERET and AR face databases show that the recognition performance for blurred face image is effectively improved by fusing the color and texture features.
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References
|
[1]
|
Shen L, Bai L, Fairhurst M. Gabor wavelets and general discriminant analysis for face identification and verification[J]. Image and Vision Computing, 2007, 25(5): 553-563. |
|
[2]
|
|
|
[3]
|
|
|
[4]
|
Ahonen T, Hadid A, Pietikainen M. Face recognition with local binary patterns [C]//Proceedings of 8th European Con-ference on Computer Vision, 2004: 469-481. |
|
[5]
|
Xie Z, Liu G. Fast thermal infrared face recognition system based on local binary pattern[J]. Infrared and Laser Engi-neering, 2013, 42(12): 3190-3195. (in Chinese) |
|
[6]
|
|
|
[7]
|
|
|
[8]
|
Hu H. Augmented DT-CWT feature based classification us-ing regularized neighborhood projection discriminant analysis for face recognition[ J]. Pattern Recognition, 2011, 44(3): 519-531. |
|
[9]
|
Wang H, Yang Q, Wang X. A fast test and recognition method for object character [J]. Infrared and Laser Engi-neering, 2014, 43(3): 1002-1008. (in Chinese) |
|
[10]
|
|
|
[11]
|
|
|
[12]
|
Choi J Y, Ro Y M, Plataniotis K N. Color face recognition for degraded face images[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2009, 39 (5): 1217-1230. |
|
[13]
|
|
|
[14]
|
Liu Z, Yang J, Liu C. Extracting multiple features in the CID color space for face recognition[J]. IEEE Transactions on Image Processing, 2010, 19(9): 2502-2509. |
|
[15]
|
|
|
[16]
|
Deng W, Hu J, Guo J, et al. Emulating biological strategies for uncontrolled face recognition [J]. Pattern Recognition, 2010, 43(6): 2210-2223. |
|
[17]
|
|
|
[18]
|
Du X, Gong W, Zhong J. Color face recognition method in-spired by biological visual mechanisms [J]. Chinese Journal of Scientific Instrument, 2012, 33(8): 1687-1696. (in Chi-nese) |
|
[19]
|
Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720. |
|
[20]
|
|
|
[21]
|
Engel S, Zhang X, Wandell B. Colour tuning in human visu-al cortex measured with functional magnetic resonance imag-ing[J]. Nature, 1997, 388(6637): 68-71. |
|
[22]
|
|
|
[23]
|
Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20 (11): 1254-1259. |
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