Fourier Spectrum Features for Face Recognition
Ascar Davix.X1, John Moses.C2, Suresh Kumar Pittala3, Eswara Chaitanya.D4
1Dr.X.Ascar Davix*, Associate Professor in the Department of Electronics & Communication Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
2Dr. C. John Moses, Associate Professor of Electronics and Communication Engineering in Sreyas Institute of Engineering and Technology, Hyderabad.
3Dr. Suresh Kumar Pittala, Associate Professor in the Department of Electronics & Communication Engineering, RVR & JC College of Engineering, Guntur, A.P, and India.
4Dr. D. Eswara Chaitanya, Associate Professor, RVR&JC College of Engineering, Guntur.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2632-3636 | Volume-9 Issue-4, February 2020. | Retrieval Number: B7469129219/2020©BEIESP | DOI: 10.35940/ijitee.B7469.029420
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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: The objective is to introduce a novel approach which deals with the challenges: uneven illumination and partial occlusion. This method performs face recognition by extracting the magnitude spectra features. At each point on the face, largest matching areas were found. Thus robustness is achieved using Fourier magnitude spectra feature extraction and largest matching area comparison. This method performs competitively with corrupted images and other unsupervised methods. The proposed approach is experimented on Yale B and AR datasets
Keywords: Face Recognition, LMA, Fourier Transform
Scope of the Article: Pattern Recognition