- Research Article
- Open Access
Information Theory for Gabor Feature Selection for Face Recognition
EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 030274 (2006)
A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.
Daugman JG: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A - Optics, Image Science, and Vision 1985, 2(7):1160–1169. 10.1364/JOSAA.2.001160
Okajima K: Two-dimensional Gabor-type receptive field as derived by mutual information maximization. Neural Networks 1998, 11(3):441–447. 10.1016/S0893-6080(98)00007-0
Kyrki V, Kamarainen J-K, Kälviäinen H: Simple Gabor feature space for invariant object recognition. Pattern Recognition Letters 2004, 25(3):311–318. 10.1016/j.patrec.2003.10.008
Phillips PJ, Moon H, Rizvi SA, Rauss PJ: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(10):1090–1104. 10.1109/34.879790
Wiskott L, Fellous J-M, Kuiger N, von der Malsburg C: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):775–779. 10.1109/34.598235
Messer K, Kittler J, Sadeghi M, et al.: Face authentication test on the BANCA database. Proceedings of 17th International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 523–532.
Lades M, Vorbruggen JC, Buhmann J, et al.: Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 1993, 42(3):300–311. 10.1109/12.210173
Liu C, Wechsler H: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 2002, 11(4):467–476. 10.1109/TIP.2002.999679
Shen L, Bai L: Gabor feature based face recognition using Kernel methods. Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition(FGR '04), May 2004, Seoul, South Korea 170–176.
Fasel IR, Bartlett MS, Movellan JR: A comparison of Gabor filter methods for automatic detection of facial landmarks. Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition(FGR '02) , May 2002, Washington, DC, USA 231–235.
Liu D-H, Lam K-M, Shen L-S: Optimal sampling of Gabor features for face recognition. Pattern Recognition Letters 2004, 25(2):267–276. 10.1016/j.patrec.2003.10.007
Campbell NW, Thomas BT: Automatic selection of Gabor filters for pixel classification. Proceeding of 6th IEE International Conference on Image Processing and Its Applications(IPA '97), July 1997, Dublin, Ireland 2: 761–765.
Sun Z, Bebis G, Miller R: Evaluationary Gabor filter optimization with application to vehicle detection. Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM '03), November 2003, Melbourne, Fla, USA 307–314.
Viola P, Jones M: Rapid object detection using a boosted cascade of simple features. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 511–518.
Shen L, Bai L: AdaBoost Gabor feature selection for classification. Proceeding of Image and Vision Computing Conference (IVCNZ '04), 2004, Akaroa, New Zealand 77–83.
Li SZ, Zhang Z: FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(9):1112–1123. 10.1109/TPAMI.2004.68
Tourassi GD, Frederick ED, Markey MK, Floyd CE Jr.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Medical Physics 2001, 28(12):2394–2402. 10.1118/1.1418724
Fleuret F: Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research 2004, 5: 1531–1555.
Weldon TP, Higgins WE, Dunn DF: Efficient Gabor filter design for texture segmentation. Pattern Recognition 1996, 29(12):2005–2015. 10.1016/S0031-3203(96)00047-7
Kruger V, Sommer G: Gabor wavelet networks for efficient head pose estimation. Image and Vision Computing 2002, 20(9–10):665–672. 10.1016/S0262-8856(02)00056-2
Phillips PJ: Support vector machines applied to face recognition. Proceedings of 1998 Conference on Advances in Neural Information Processing Systems II, November 1999 803–809.
Scholkopf B, Mika S, Burges CJC, et al.: Input space versus feature space in Kernel-based methods. IEEE Transactions on Neural Networks 1999, 10(5):1000–1017. 10.1109/72.788641
Yang M-H: Kernel eigenfaces vs. Kernel fisherfaces: face recognition using Kernel methods. Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '02), May 2002, Washington, DC, USA 215–220.
Baudat G, Anouar F: Generalized discriminant analysis using a Kernel approach. Neural Computation 2000, 12(10):2385–2404. 10.1162/089976600300014980
Belhumeur PN, Hespanha JP, Kriegman DJ: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(7):711–720. 10.1109/34.598228
Kendall M, Stuart A, Ord JK: Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory. Edward Arnold, Paris, France; 1994.
Beveridge R, Draper B: Evaluation of Face Recognition Algorithms. 2003.
Kepenekci B, Tek FB, Akar GB: Occluded face recognition based on Gabor wavelets. Proceedings of the IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 1: 293–296.
About this article
Cite this article
Shen, L., Bai, L. Information Theory for Gabor Feature Selection for Face Recognition. EURASIP J. Adv. Signal Process. 2006, 030274 (2006). https://doi.org/10.1155/ASP/2006/30274
- Feature Selection
- Mutual Information
- Face Recognition
- Face Image
- Kernel Method