The week's pick
Random Articles
Reseach Article
Face Recognition using SIFT by varying Distance Calculation Matching Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 47 - Number 3 |
| Year of Publication: 2012 |
| Authors: Hirdesh Kumar, Padmavati |
10.5120/7168-9739
|
Hirdesh Kumar, Padmavati . Face Recognition using SIFT by varying Distance Calculation Matching Method. International Journal of Computer Applications. 47, 3 ( June 2012), 20-26. DOI=10.5120/7168-9739
Abstract
Scale Invariant Feature Transform (SIFT) is a method for extracting distinctive invariant feature from images [1]. SIFT has been applied to many problems such as face recognition and object recognition [18], [19], [20], [21]. We have analyzed performance of SIFT using Euclidean distance as a matching algorithm. Further the matching rate can be enhanced/improved by changing distance calculation methods used for matching between two face images. So this paper also describes face recognition under various distance calculation methods like Correlation and Cosine. The experiments are conducted on different images of ORL face database [17] and Indian Face database [16] by changing illumination condition, scaling and rotation. From the experiments, it is shown that cosine and correlation distance calculation methods have performed well compared to the Euclidean distance matching method of original SIFT.
References
- Lowe, D. 2004 "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, Vol. 60, no. 2, 91–110.
- Mikolajczyk, K. and Schmid, C. 2002 "An affine invariant interest point detector", In European Conference on Computer Vision,(ECCV), Copenhagen, Denmark, 128–142.
- Lindeberg, T. 1994 "Scale-space theory: A basic tool for analyzing structures at different scales", Journal of Applied Statistics, vol. 21, no. 2, 224–270.
- Beis, J. and Lowe, D. 1997 "Shape indexing using approximate nearest-neighbor search in high-dimensional spaces", In Proceedings of the International Conference on Computer Vision and Pattern Recognition, 1000–1006.
- Brown, M. and Lowe, D. 2002 "Invariant Features from Interest Point Groups", In Proceedings of the 13th British Machine Vision Conference, Cardiff, 253–262.
- Lowe, D. 1999 "Object Recognition from Local Scale Invariant Features", In Proceedings of the International Conference on Computer Vision, Corfu, Greece, 1150–1157.
- Schmid, C. and Mohr, R. 1997 "Local Gray value Invariants for Image Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, 530–535.
- Mikolajczyk, K. and Schmid, C. 2005 "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1615–1630.
- Fergus, R. , Perona, P. and Zisserman, A. 2003 "Object class recognition by unsupervised scale-invariant learning", In IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, 264–271.
- Lowe, D. 2001 "Local feature view clustering for 3D object recognition", IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, 682–688.
- Ledwich, L. and Williams, S. "Reduced SIFT Features For Image Retrieval and Indoor Localisation", Australasian Conf. on Robotics and Automation ACRA, Canberra.
- Strehl, A. , Ghosh, J. and Mooney, R. 2000 "Impact of similarity measures on web-page clustering", In AAAI-2000: Workshop on Artificial Intelligence for Web Search.
- Salton, G. 1989 "Automatic Text Processing", Addison-Wesley, New York.
- Huang, A. 2008 "Similarity Measures for Text Document Clustering", NZCSRSC 2008.
- Distance calculation methods used for matching are taken from following URL: http://www. mathworks. com/help/toolbox/stats/pdist2. html. Jain, V. and Mukherjee, A. 2002 "The Indian Face Database" http://vis-www. cs. umass. edu/~vidit/IndianFaceDatabase/
- The ORL face database is available for download on the Internet through Olivetti Research Limited's web server at: http://www. cl. cam. ac. uk/research/dtg/attarchive/pub/data/att_faces. zip
- Aly, M. 2006 "Face recognition using sift features", CNS186 Term Project Winter 2006.
- Krizaj, J. , Struc, V. and Pavesic, N. M. 2010 "Adaptation of SIFT features for face recognition under varying illumination", Proceedings of the 33rd International Convention, pp. 691-694.
- Majumdar, A. and Ward, R. K. , 2009 "Discriminative SIFT features for face recognition", Canadian Conference on Digital Object Identifier, pp. 27-30.
- Wang, H. , Yang, K. , Gao, F. and Li, J. 2011 "Normalization Methods of SIFT Vector for Object Recognition", Tenth International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. -175-178.
Index Terms
Keywords