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Monogenic scale space based region covariance matrix descriptor: an efficient and accurate face recognition algorithm

Published: 03 August 2012 Publication History

Abstract

In this paper, we present a new face recognition algorithm based on region covariance matrix (RCM) descriptor computed in monogenic scale space. In the proposed model, local energy information and local phase information obtained using monogenic filter is used to represent a pixel at different scales to form region covariance matrix descriptor for each face image during training phase. An eigen-value based distance measure is used to compute the similarity between face images. Extensive experimentation on AT&T and YALE face database has been conducted to reveal the performance of the monogenic scale space based region covariance matrix method and comparative analysis is made with the basic RCM method and Gabor Wavelet based region covariance matrix method to exhibit the superiority of the proposed technique.

References

[1]
Bay H, Ess A, Tuytelaars T, Gool L V, 2008. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, Vol. 110, Issue 3, Pp. 346--359.
[2]
Belhumeur, P. N., J. P. Hespanha, and D. J. Kreigman, 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19(7), pp. 711--720.
[3]
Felsberg, M. and Sommer, G. 2004. The monogenic scale-space: A unifying approach to phase-based image processing in scalespace. Journal of Mathematical Imaging and Vision, vol. 21(1), pp. 5--26.
[4]
Felsberg, M., and G. Sommer, 2001. The monogenic signal, IEEE Transactions on Signal Processing, vol. 49, pp. 3136--3144.
[5]
Fergus, R., P. Perona, and A. Zisserman, 2003. Object class recognition by unsupervised scale-invariant learning. In Proceedings of Computer Vision and Pattern Recognition.
[6]
Forstner, W. and Moonen, B, 1999. A metric for covariance matrices. Technical report, Dept. of Geodesy and Geoinformatics, Stuttgart University.
[7]
Freeman, W. T. and E. H. Adelson, 1991. The design and use of steerable filters. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13(9), pp. 891--906.
[8]
Harris, C. and M. Stephens, 1988. A combined corner and edge detector, In Alvey Vision Conference, pages 147--151.
[9]
Ke, Y. and R. Sukthankar, 2004. PCA-SIFT: A more distinctive representation for local image descriptors, In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Washington.
[10]
Koenderink, J. and A. van Doorn, 1987. Representation of local geometry in the visual system. In Biological Cybernetics, volume 55, pages 367--375.
[11]
Lades, M., J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Wurtz, and W. Konen, 1993. Distortion Invariant Object Recognition in the Dynamic Link Architecture, IEEE Transactions on Computers, vol. 42, pp. 300--311.
[12]
Liu, C, 2004. Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 5.
[13]
Lowe, D. G., 2004. Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision.
[14]
Lowe, D. G., 1999. Object recognition from local scale-invariant features, In Proceedings of International Conference on Computer Vision, pp. 1150--1157.
[15]
Mikolajczyk, K and C. Schmid, 2003. A performance evaluation of local descriptors, In Proceedings of Computer Vision and Pattern Recognition.
[16]
Mikolajczyk, K and C. Schmid, 2001. Indexing based on scale invariant interest points. In Proceedings of International Conference on Computer Vision, pages 525--531.
[17]
Oppenheim, A. V. and 1. S. Lim, 1981. The importance of phase in signals, Proceedings of The IEEE, vol. 69, no. 5.
[18]
Poriklim F and O. Tuzel, 2006. Fast construction of covariance matrices for arbitrary size image windows, In Proceedings of IEEE International Conference on Image Processing, 2006, pp. 1581--1584.
[19]
Schokopf, B., S. Mika, C. J. C Burges, P. Knirsch, K.-R Muller, G. Ratsch, and A. J. Mola., 1999. Input space versus feature space in kernel based methods, IEEE Transactions on Neural Networks, vol. 10(5), pp. 1299--1319.
[20]
Turk, M. and A. Pentland, 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, vol. 3(1), pp. 71--86.
[21]
Tuzel, O., F. Porikli, and P. Meer, 2006. Region covariance: a fast descriptor for detection and classification, In the Proceedings of European Conference on Computer Vision, pp. 589--600.
[22]
Van Gool, L., T. Moons, and D. Ungureanu, 1996. Affine/photometric invariants for planar intensity patterns. In Proceedings of European Conference on Computer Vision.
[23]
Wiskott, L., J. M. Fellous, N. Kruger, and C. von der Malsburg, 1997. Face Recognition by Elastic Bunch Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775--779.
[24]
Yanwei P., Yuan Yuan and Xuelong Li., 2008. Gabor-based region covariance matrices for face recognition, IEEE Transactions on circuits and systems for video technology, vol. 18(7).
  1. Monogenic scale space based region covariance matrix descriptor: an efficient and accurate face recognition algorithm

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        ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
        August 2012
        1307 pages
        ISBN:9781450311960
        DOI:10.1145/2345396
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 03 August 2012

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        Author Tags

        1. Riesz transform
        2. face recognition
        3. log-Gabor transform
        4. monogenic scale space
        5. region covariance matrix descriptor

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