Fuzzy Shannon Entropy for Face Recognition
Abdullah Gubbi1, Mohammad Fazle Azeem2, Mohammed Rafeeq3, Shoaib Kamal4
1Abdullah Gubbi, Department of Electronics and Communication Engineering, Bearys Institute of Technology, Mangalore, India.
2Mohammad Fazle Azeem, Department of Electrical Engineering, King Khalid University, Aseer Province , Saudi Arabia.
3Mohammed Rafeeq, Department of Electronics and Communication Engineering, Bearys Institute of Technology, Mangalore, India.
4Shoaib Kamal, Department of Electronics and Communication Engineering, Bearys Institute of Technology, Mangalore, India.
Manuscript received on 13 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript Published on 26 July 2019 | PP: 1159-1163 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12400486S419/19©BEIESP | DOI: 10.35940/ijitee.F1240.0486S419
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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: This paper presents a new local feature called Fuzzy Shannon entropybased features for face recognition. These features are computationally simple and capture the variation in the face image. The new Fuzzy membership functions are defined which are not like standard Gaussian or trapezoidal membership functions, but they are computed with normalized Image intensity values. This paper gives the flexibility to design of new membership functions to cater the need of the problem. The Information set is the combination of fuzzy membership and Information source (attributes). By applying this concept, we have extracted features from ORL and Faces94 databases with Support Vector Machine and K Nearest Neighbour classifier the results obtained with the proposed method are better than other published contemporary techniques.
Keywords: Face Recognition, Fuzzy Logic, Shannon Entropy, K- Nearest Neighbour, Support Vector Machine and Information set.
Scope of the Article: Fuzzy Logic