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A Novel Face Matching Technique using Mean-Shift with Region Merging
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 63 - Number 10 |
| Year of Publication: 2013 |
| Authors: Shahab Ahmed, Zeeshan Khan, Anurag Jain |
10.5120/10500-5266
|
Shahab Ahmed, Zeeshan Khan, Anurag Jain . A Novel Face Matching Technique using Mean-Shift with Region Merging. International Journal of Computer Applications. 63, 10 ( February 2013), 7-13. DOI=10.5120/10500-5266
Abstract
We proposed a novel method for face matching from face image database. In our method we have taken set of face images so recognition decisions need to be based on comparisons of face image database. This paper presents an approach to region based face matching. The low level image segmentation method mean shift is used to divide the image into many small regions. As a popular segmentation scheme for color image, watershed has over segmentation as compared to mean-shift and also mean-shift preserves well the edge information of the object. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, effectively extracts the object contour and then, matches the obtained mask with test database image sets on the basis of color and texture. Extensive experiments are performed and the results show that the proposed scheme can reliably form the mask from the face image and effectively matches the mask with face image sets.
References
- Bo Peng, Lei Zhang and David Zhang, Automatic Image Segmentation by Dynamic Region Merging, ," IEEE Trans. On Image Processing. , vol. 20, no. 12, pp. 679–698, DEC 2011
- F. Lecumberry, A. Pardo, and G. Sapiro, "Simultaneous object classifi- cation and segmentation with high-order multiple shape models," IEEE Trans. Image Process. , vol. 19, no. 3, pp. 625–635, Mar. 2010.
- J. Stawiaski and E. Decenciere, "Region Merging via Graph-cuts," in Image Anal Stereol, 2008;27, pp. 39-45.
- J. Ning, L. Zhang, D. Zhang, and C. Wu, "Interactive image segmentation by maximal similarity based region merging," Pattern Recognit. , vol. 43, no. 2, pp. 445–456, Feb. 2010.
- F. Calderero and F. Marques, "General region merging approaches based on information theory statistical measures," in Proc. 15th IEEE ICIP, 2008, pp. 3016–3019.
- K. Haris, S. N. Estradiadis, N. Maglaveras, and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging," IEEE Trans. Image Process. , vol. 7, no. 12, pp. 1684–1699, Dec. 1998
- P. V. G. D. Prasad Reddy, K. Srinivas Rao and S. Yarramalle, "Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM and K-Means Algorithm," Proceedings of International Journal of Computer Science and Network Security, vol. 7, no. 4, pp. 317-321, April 2007.
- Z. Shi and V. Govindaraju, "Historical Handwritten Document Image Segmentation using Background Light Intensity Normalization," SPIE Proceedings on Center of Excellence for Document Analysis and Recognition, Document Recognition and Retrieval, vol. 5676, pp. 167-174, January 2005.
- P. F. Felzenswalb and D. P. Huttenlocher, "Efficient Graph- Based Image Segmentation," Proceedings of International Journal of Computer Vision, vol. 59, no. 2, pp. 167-181, 2004.
- A. Mavrinac, "Competitive Learning Techniques for Color Image Segmentation," Proceedings of the Machine Learning and Computer Vision, vol. 88, no. 590, pp. 33-37, April 2007.
- Y. Li, J. Sun, C. Tang, H. Shum, Lazy snapping, SIGGRAPH 23 (2004) 303–308.
- E. Sharon, A. Brandt and R. Basri, "Fast Multi-Scale Image Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 70-77, 2000
- L. O. Donnell, C. F. Westin, W. E. L. Grimson, J. R. Alzola, M. E. Shenton and R. Kikinis, "Phase-Based user Steered Image Segmentation," Proceedings of the Fourth International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 1022-1030, 2001
- J. Malik, S. Belongie, J. Shi and T. Leung, "Textons, Contours and Regions: Cue Integration in Image Segmentation," Proceedings of Seventh International Conference on Computer Vision, pp. 918-925, September 1999.
- J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, 2000.
- Hakan Cevikalp and Bill Triggs, "Face recognition based on Image Sets,"IEEE Conference on Computer Vision and Pattern Recognition, San Francisco : United States(2010)"
- Costas Panagiotakis, Ilias Grinias, and Georgeios Tziritas "Natural Image Segmentaion Based on Tree Equipartition, Bayesian Flooding and Region Merging", IEEE Transactions on Image Processing, Vol. 20, No. 8, August 2011.
- Lei Zhang and Qiang Ji, "A Bayesian Network Model for Automatic and Interactive Image Segmentation", IEEE Transaction on Image Processing, VOL. 20, NO. 9, September 2011.
- S. Birchfield, Elliptical head tracking using intensity gradients and color histograms, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1998, pp. 232-237.
- T. Ojala, M. Pietikainen, P. Maenpaa Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactionson Pattern Analysis and Machine Intelligence, 2002, pp. 971-987.
- M. J. Swain, D. H. Ballard, "Color indexing" ,International Journal of Computer Vision Vol. 7 No. 1, 2002, pp. 11-32.
- D. Comaniciu, V. Ramesh, P. Meer, "Kernel-based object tracking", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, pp. 564-577.
- D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentationalgorithms and measuring ecological statistics," in Proc. ICCV, 2001, pp. 416–423.
- Y. Cheng, "Mean shift, mode seeking, and clustering", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, 1995, pp. 790–799.
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