Identifying the Faces from Poor Quality Image / Video
T.Shreekumar1, K.Karunakara2
1T.ShreeKumar*, Research Scholar, Dept. CS&E, Sri Siddartha Academy of Higher Education, Tumkur, Karnataka, India.
2K.Karunakara, Prof &Head( Dept.IS&E), Dept.IS&E,SriSiddartha Institute of Tehnology, Sri Siddartha Academy of Higher Education,Tumkur, Karnataka, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1346-1353 | Volume-8 Issue-12, October 2019. | Retrieval Number: L39251081219/2019©BEIESP | DOI: 10.35940/ijitee.L3925.1081219
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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: Face biometric is becoming more popular because of its wide range of applications in authorizing the person either from an image or from the video sequence. The bottleneck in face recognition is Pose angle variation, varying light condition, Partial Occlusion, Blur in the image or Noise. The proposed method first removes the noise from the image using Adaptive Median Filter (AMF) then Discrete Cosine Transform(DCT) is applied to normalize the illumination problem. The algorithm is further used to remove the motion blur using Lucy Richardson’s method by calculating the Point Spread Function (PSF). The Pose variation problem is then addressed with Global Linear Regression(GLR). Then the Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are applied to the normalized image to get the feature vector. This combined feature score is used to recognize the image using K-Nearest Neighbor (K-NN). The result shows a maximum accuracy of 92% and 87.5% with Pose angle variation between (0°, 22°) and (22°, 40°) respectively. The pose variation greater than this shows an average accuracy of 77.5%. The result also shows a maximum computation speed of 0.018 Seconds.
Keywords: Face Recognition, Linear Regression, Principal Component Analysis.
Scope of the Article: Pattern Recognition