A new approach for face recognition from video sequence

Prasanna, Kumar M.K.; Shantharama, C.

doi:10.1109/icisc.2018.8398969

Cited by 4 publications

(1 citation statement)

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“…Consequently the virtual view generation may be decomposed into 2 steps: first one is solving the reconstruction coefficients in the pose image by the equation (25). The second step is predicting the final virtual frontal view with the equation (24). After Minimizing the following residue function:…”

Section: Virtual Virtual Frontal View Generationmentioning

confidence: 99%

Identifying the Faces from Poor Quality Image / Video

kumar*¹,

Karunakara²

2019

IJITEE

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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.

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Section: Virtual Virtual Frontal View Generationmentioning

confidence: 99%