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Total variation models for variable lighting face recognition
Abstract
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions.
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Cites
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., & Fatemi, E. (1992) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 15,704
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
Self-citationBilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“To reduce halo artifacts, discontinuity preserving filtering can be used to estimate L, such as anisotropic diffusion [28], bilateral filtering [40], or mean shift filtering [11].”
MethodFrom few to many: illumination cone models for face recognition under variable lighting and pose
Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927464 · Cited by 4,912
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
(1986) · Cited by 3,616
A multiscale retinex for bridging the gap between color images and the human observation of scenes
Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.597272 · Cited by 3,036
Face recognition by elastic bunch graph matching
Wiskott, L., Fellous, J.-M., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,883
Lambertian reflectance and linear subspaces
Basri, R., & Jacobs, D. W. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1177153 · Cited by 1,576
Fast bilateral filtering for the display of high-dynamic-range images
Durand, F., & Dorsey, J. (2002) · Proceedings of the 29th annual conference on Computer graphics and interactive techniques · doi:10.1145/566570.566574 · Cited by 1,547
Alizadeh, F., & Goldfarb, D. (2003) · Mathematical Programming · doi:10.1007/s10107-002-0339-5 · Cited by 1,500
The CMU Pose, Illumination, and Expression (PIE) database
Sim, T., Baker, S., & Bsat, M. (2003) · Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition · doi:10.1109/afgr.2002.1004130 · Cited by 1,419
Face recognition: the problem of compensating for changes in illumination direction
Adini, Y., Moses, Y., & Ullman, S. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598229 · Cited by 1,059
Fast bilateral filtering for the display of high-dynamic-range images
Durand, F., & Dorsey, J. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566574 · Cited by 1,003
Aspects of Total Variation Regularized L1Function Approximation
Chan, T. F., & Esedoglu, S. (2005) · SIAM Journal on Applied Mathematics · doi:10.1137/040604297 · Cited by 651
Determining lightness from an image
Horn, B. K. P. (1974) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(74)90022-7 · Cited by 635
Local feature analysis: a general statistical theory for object representation
Penev, P. S., & Atick, J. J. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/3/002 · Cited by 627
Automatic interpretation and coding of face images using flexible models
Lanitis, A., Taylor, C. J., & Cootes, T. F. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598231 · Cited by 625
The quotient image: class-based re-rendering and recognition with varying illuminations
Shashua, A., & Riklin-Raviv, T. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908964 · Cited by 615
Moghaddam, B., Jebara, T., & Pentland, A. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00179-x · Cited by 612
Edge-preserving and scale-dependent properties of total variation regularization
Strong, D., & Chan, T. (2003) · Inverse Problems · doi:10.1088/0266-5611/19/6/059 · Cited by 597
A signal-processing framework for inverse rendering
Ramamoorthi, R., & Hanrahan, P. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383271 · Cited by 577
Face recognition by support vector machines
Guo, G., Li, S. Z., & Chan, K. (2002) · Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) · doi:10.1109/afgr.2000.840634 · Cited by 520
Image processing in the context of a visual model
Stockham, T. G. (1972) · Proceedings of the IEEE · doi:10.1109/proc.1972.8782 · Cited by 498
LCIS: a boundary hierarchy for detail-preserving contrast reduction
Tumblin, J., & Turk, G. (1999) · Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99 · doi:10.1145/311535.311544 · Cited by 401
What is the set of images of an object under all possible lighting conditions?
Belhumeur, P. N., & Kriegman, D. J. (1996) · Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1996.517085 · Cited by 379
Ramamoorthi, R., & Hanrahan, P. (2001) · Journal of the Optical Society of America A · doi:10.1364/josaa.18.002448 · Cited by 377
Illumination normalization for robust face recognition against varying lighting conditions
Shan, S., Gao, W., Cao, B., & Zhao, D. (2003) · 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443) · doi:10.1109/amfg.2003.1240838 · Cited by 358
An Image Preprocessing Algorithm for Illumination Invariant Face Recognition
Gross, R., & Brajovic, V. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44887-x_2 · Cited by 343
Support vector machines for face recognition
Guo, G., Li, S. Z., & Chan, K. L. (2001) · Image and Vision Computing · doi:10.1016/s0262-8856(01)00046-4 · Cited by 338
Nikolova, M. (2002) · SIAM Journal on Numerical Analysis · doi:10.1137/s0036142901389165 · Cited by 326
Second-order Cone Programming Methods for Total Variation-Based Image Restoration
Goldfarb, D., & Yin, W. (2005) · SIAM Journal on Scientific Computing · doi:10.1137/040608982 · Cited by 196
Self-citationWang, H., Li, S. Z., & Wang, Y. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315205 · Cited by 170
Face recognition under variable lighting using harmonic image exemplars
Zhang, L., & Samaras, D. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211333 · Cited by 141
Digital filters as absolute norm regularizers
Alliney, S. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.139258 · Cited by 116
A comparison of photometric normalisation algorithms for face verification
Short, J., Kittler, J., & Messer, K. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301540 · Cited by 80
Robust image based face recognition
Zhao, W. Y., & Chellappa, R. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2000.900887 · Cited by 51
Brightness perception,dynamic range and noise: a unifying model for adaptive image sensors
Brajovic, V. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315163 · Cited by 27
Cited by
Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., & Ma, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.112 · Cited by 629
Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model
Tan, X., Li, Y., Liu, J., & Jiang, L. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15567-3_37 · Cited by 521
Metadata
- Authors
- Chen, T., Yin, W., Zhou, X. S., Comaniciu, D., & Huang, T. S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-09
- Volume
- 28
- Issue
- 9
- Pages
- 1519-1524
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2146772029
- Cited by
- 437
- References
- 41
- FWCI
- 23.92
- Type
- journal article
- Language
- en
- Open access
- closed
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