Gender Classification using Central Fibonacci Weighted Neighborhood Pattern Flooding Binary Matrix (CFWNP_FBM) Shape Primitive Features
P.Chandra Sekhar Reddy1, G R Sakthidharan2, S. Kanimozhi Suguna3, J. Mannar Mannan4, P Varaprasada Rao5
1Dr. P.Chandra Sekhar Reddy*, CSE Dept., Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, (Telangana), India.
2Dr. G R Sakthidharan, CSE Dept., Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, (Telangana), India.
3Dr. S. Kanimozhi Suguna, School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India.
4Dr. J. Mannar Mannan, Dept. of information science and engineering, MVJ College of Engineering, Bangalore, India.
5Dr. P Varaprasada Rao, Dept. of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, (Telangana), India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5238-5244 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9284088619/2019©BEIESP | DOI: 10.35940/ijeat.F9284.088619
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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: Gender Classification from facial images is an open research area with wide range of computer vision applications like security, biometrics and human computer interaction applications. In the proposed method the LL band image of facial image is obtained by using wavelet then on this image Fibonacci Weighted Neighborhood Central pixel Flood binary Matrix is computed and then shape features are evaluated. SVM method uses these shape features for gender classification. The proposed approach has been experimented on FG NET database. The experimental results has shown the more accuracy compared to with other existing methods.
Keywords: Gender Classification, biometrics, Fibonacci Weighted Neighborhood Central pixel Flood binary, FG NET.