Face Recognition Using Support Vector Data Description and K-Means Clustering
Rahul Choudhary1, S. Kalaiarasi2, Aditya Verma3, Vishal Srivastava4, L. Satish Kumar5
1Rahul Choudhary, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2S. Kalairasi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Aditya Verma, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Vishal Srivastava, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
5L. Satish Kumar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1261-1264 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3880048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We present Face Recognition using k-means algorithm for clustering and support vector data description. This unification of these two algorithms enables the two techniques to take advantage of each other, i.e., by including adaptability utilizing various spheres for SVDDs and expanding anomaly resistance and adaptability through kernels to k-means. By changing over pictures of human facial features into feature distinguishable images, which are a little arrangement of characteristics and features pictures, we dispose of the excess and safeguard the fluctuation in few coefficients. For image identification, the test picture is anticipated in a lower measurement vector as a representation of eigenfaces. We take the anticipated picture of the test set and analyze it with the training set, utilizing the Euclidian distance. We evaluate our methodology using the F-measures (F1 score).
Keyword: Face Recognition, K-Means Clustering, SVDD.
Scope of the Article: Clustering