An adaptive K-Means Clustering Algorithm and its Application to Face Recognition
|Published in:||Issue 3, (Vol. 4) / 2010|
|Author(s):||RAJALAKSHMI K., THILAKA B., RAJESWARI N.|
|Abstract.||Pattern recognition is an emerging research area that studies the operation and design of systems that recognize patterns in data. Clustering is an essential and very frequently performed task in pattern recognition and data mining. Clustering refers to the process of grouping samples so that the samples are similar within each group. The groups are called clusters. k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set of n points xi through a certain number of clusters fixed a priori. The difficulty in implementing k-means method for a large database is in determining the number of clusters which has to be randomly chosen. To overcome this difficulty, we propose a variation of the k-means algorithm, where the number of clusters ‘k’ can change dynamically depending on the data points and a threshold value given as an input. The proposed algorithm is applied in face recognition which is a very complex form of pattern recognition .It is used to verify whether a test face belongs to the database of faces and if so, identifies it.|
|Keywords:||K-means Clustering Algorithm, Eigenfaces.|
ď»ż1. Sirovich L and Kirby M, March 1987, â€śLow dimensional procedure for the characterization of human facesâ€ť, J.Opt.Soc.Am.A, vol.4,No.3, 519-524.
2. Matthew A. Turk and Alex P. Pentland, 1991,"Eigenfaces for recognition", J. of Cognitive Neuroscience, Vol. 3, No. 1, 71-86
3. Tapas Kanungo, David M..Mount, Natan S. Netanyahu, Christing D. Piatko, Ruth Silverman,Angela Y. Wu , July 2002, â€śAn EfficientK-means Clustering Algorithm: Analysis and Implementationâ€ť, IEEE
4. Earl Gose, Richard Johnsonbaugh and Steve Jost, 2005, â€śPattern Recognition and Image Analysisâ€ť, Prentice Hall of India Pvt. Ltd.
5. Kresimir Delac, Mislav Grgic, Panos Liatsis, 2005, â€śAppearance-based Statistical Methods for Recognitionâ€ť 47th International Symposium ELMAR-2005, 08-10 June 2005, Zader, Croatia, 151-158
6. Yang Xinhua, Yu Kuan and Deng Wu, August 2006 ,â€śA k-means Clustering Algorithm based on Self-Adoptively Selecting Density Radiusâ€ť International Journal of Computer Science and Network Security, Vol. 6 No. 8A, 43-46, Machine Intelligence, Vol. 24, No 7, 881-892.
|Back to the journal content|
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.