Local Steerable Pyramid Binary Pattern Sequence LSPBPS for Face Recognition Method
In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
[1] Li Bai Linlin Shen, A Review on Gabor Wavelets for Face Recognition,
Pattern Analysis and Applications, vol. 9, no. 2, pp. 273292, 2006.
[2] A Bouridane W. R. Boukabou, Contourlet-based feature extraction with
pca for face recognition, NASA/ESA Conference on Adaptive Hardware
and Systems, 2008.
[3] A. Majumdar T. Mandal and Q.M. Jonathan Wu., Face recognition by
curvelet based feature extraction, ICIAR - International Conference on
Image Analysis and Recognition, vol. LNCS 4633, pp. 806817, 2007.
[4] M. El Aroussi, S. Ghouzali, M. El Hassouni, M .Rziza, and D. Aboutajdine,
Curvelet-Based Feature Extraction with B-LDA for Face Recognition,
The 7th ACS/IEEE International Conference on Computer Systems
and Applications (AICCSA)., 2009.
[5] R.H. Bamberger and M. J. T. Smith, Filter Bank for the Directional
Decomposition of Images: Theory and Design, IEEE Trans. Pattern
Analysis and Machine Intelligence, vol. SP-40(4), pp. 882893, 1992.
[6] M Wainwright E P Simoncelli J Portilla, V Strela, Image Denoising using
Scale Mixtures of Gaussians in the Wavelet Domain,, IEEE Trans. Image
Processing, vol. 12, pp. 13381351, 2003.
[7] S. Li and J. Shawe-Taylor, Comparison and fusion of multiresolution
features for texture classification, Pattern Recognition Letters, vol. 25,
2002.
[8] Li Huanga FeiWua Congyong Sua, Yueting Zhuanga, Steerable pyramidbased
face hallucination, IEEE Trans. Pattern Recognition, vol. 28, pp.
813824, 2005.
[9] E P Simoncelli, A Rotation-Invariant Pattern Signature, IEEE Intl Conf on
Image Processing, Laussanne Switzerland Laussanne Switzerland, 1996.
[10] B.; Tsakalides P. Tzagkarakis, G.; Beferull-Lozano, Rotationinvariant
texture retrieval with gaussianized steerable pyramids, IEEE Trans. Image
Processing, vol. 15, no. 9, pp. 27022718, 2006.
[11] A.N. Venetsanopoulos S.Z. Li J. Lu, K.N. Plataniotis, Ensemblebased
Discriminant Learning with Boosting for Face Recognition, IEEE Transactions
on Neural Networks, vol. 17, no. 1, pp. 166178, 1 2006.
[12] E.H. Adelson W.T. Freeman, The design and use of steerable filters,
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9,
pp. 891906, 2000.
[13] Moon H. Rauss P.J. Rizvi S. Phillips, P.J., The FERET evaluation
methodology for face recognition algorithms, IEEE Trans. Pattern Analysis
and Machine Intelligence, vol. 22, no. 10, pp. 891906, 10 2000.
[14] T. Ojala, M. Pietikinen, and T. Menp. Multiresolution gray-scale and
rotation invariant texture classification with local binary patterns. PAMI,
24(7):971-987, 2002.
[15] A. Timo, H. Abdenour, and P. Matti. Face recognition with Local Binary
Patterns. In ECCV 2004, pages 469-481, 2004.
[16] Ojala T, Pietikinen M & Harwood D A comparative study of texture
measures with classification based on featured distribution. Pattern Recognition,
29(1),pp 51-59, 1996.
[17] Congyong Su, Yueting Zhuang, Li Huang, Fei Wu Steerable pyramidbased
face hallucination. Pattern Recognition, Vol. 38, No. 6. (June 2005),
pp. 813-824.
[18] W. T. Freeman and E. H. Adelson,The design and use of steerable filters,
IEEE Trans. on PAMI, vol. 13, pp. 891-906, Sept. 1991.
[1] Li Bai Linlin Shen, A Review on Gabor Wavelets for Face Recognition,
Pattern Analysis and Applications, vol. 9, no. 2, pp. 273292, 2006.
[2] A Bouridane W. R. Boukabou, Contourlet-based feature extraction with
pca for face recognition, NASA/ESA Conference on Adaptive Hardware
and Systems, 2008.
[3] A. Majumdar T. Mandal and Q.M. Jonathan Wu., Face recognition by
curvelet based feature extraction, ICIAR - International Conference on
Image Analysis and Recognition, vol. LNCS 4633, pp. 806817, 2007.
[4] M. El Aroussi, S. Ghouzali, M. El Hassouni, M .Rziza, and D. Aboutajdine,
Curvelet-Based Feature Extraction with B-LDA for Face Recognition,
The 7th ACS/IEEE International Conference on Computer Systems
and Applications (AICCSA)., 2009.
[5] R.H. Bamberger and M. J. T. Smith, Filter Bank for the Directional
Decomposition of Images: Theory and Design, IEEE Trans. Pattern
Analysis and Machine Intelligence, vol. SP-40(4), pp. 882893, 1992.
[6] M Wainwright E P Simoncelli J Portilla, V Strela, Image Denoising using
Scale Mixtures of Gaussians in the Wavelet Domain,, IEEE Trans. Image
Processing, vol. 12, pp. 13381351, 2003.
[7] S. Li and J. Shawe-Taylor, Comparison and fusion of multiresolution
features for texture classification, Pattern Recognition Letters, vol. 25,
2002.
[8] Li Huanga FeiWua Congyong Sua, Yueting Zhuanga, Steerable pyramidbased
face hallucination, IEEE Trans. Pattern Recognition, vol. 28, pp.
813824, 2005.
[9] E P Simoncelli, A Rotation-Invariant Pattern Signature, IEEE Intl Conf on
Image Processing, Laussanne Switzerland Laussanne Switzerland, 1996.
[10] B.; Tsakalides P. Tzagkarakis, G.; Beferull-Lozano, Rotationinvariant
texture retrieval with gaussianized steerable pyramids, IEEE Trans. Image
Processing, vol. 15, no. 9, pp. 27022718, 2006.
[11] A.N. Venetsanopoulos S.Z. Li J. Lu, K.N. Plataniotis, Ensemblebased
Discriminant Learning with Boosting for Face Recognition, IEEE Transactions
on Neural Networks, vol. 17, no. 1, pp. 166178, 1 2006.
[12] E.H. Adelson W.T. Freeman, The design and use of steerable filters,
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9,
pp. 891906, 2000.
[13] Moon H. Rauss P.J. Rizvi S. Phillips, P.J., The FERET evaluation
methodology for face recognition algorithms, IEEE Trans. Pattern Analysis
and Machine Intelligence, vol. 22, no. 10, pp. 891906, 10 2000.
[14] T. Ojala, M. Pietikinen, and T. Menp. Multiresolution gray-scale and
rotation invariant texture classification with local binary patterns. PAMI,
24(7):971-987, 2002.
[15] A. Timo, H. Abdenour, and P. Matti. Face recognition with Local Binary
Patterns. In ECCV 2004, pages 469-481, 2004.
[16] Ojala T, Pietikinen M & Harwood D A comparative study of texture
measures with classification based on featured distribution. Pattern Recognition,
29(1),pp 51-59, 1996.
[17] Congyong Su, Yueting Zhuang, Li Huang, Fei Wu Steerable pyramidbased
face hallucination. Pattern Recognition, Vol. 38, No. 6. (June 2005),
pp. 813-824.
[18] W. T. Freeman and E. H. Adelson,The design and use of steerable filters,
IEEE Trans. on PAMI, vol. 13, pp. 891-906, Sept. 1991.
@article{"International Journal of Electrical, Electronic and Communication Sciences:58479", author = "Mohamed El Aroussi and Mohammed El Hassouni and Sanaa Ghouzali and Mohammed Rziza and Driss Aboutajdine", title = "Local Steerable Pyramid Binary Pattern Sequence LSPBPS for Face Recognition Method", abstract = "In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
", keywords = "Face recognition (FR), Steerable pyramid (SP), localBinary Pattern (LBP).", volume = "3", number = "11", pages = "2094-4", }
