What is it about?

Five techniques are analyzed for Face Recognition in the presence of various noises and blurring effects

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Why is it important?

To develop a new novel technique which can perform efficient Face Recognition we need to analyze existing Face Recognition techniques.

Perspectives

In this paper we have developed and analyzed Minimum Average Correlation Energy Gabor Filters (MACE GF), Gabor Wavelets (GW), Discrete Cosine Transform Neural Network (DCT NN), Hybrid Spatial Feature Interdependence Matrix (HSFIM), Score Level Fusion Techniques (SLFT) for Face Recognition in the presence of various noises and blurring effects. All the 5 systems were trained in the absence of noise, blurring effect but tested by imposing different levels of noises and blurring effects. To compare the performance of MACE GF, GW, DCT NN, HSFIM, and SLFT six public face databases: IITK, ATT, JAFEE, CALTECH, GRIMACE, and SHEFFIELD are considered

Mr Steven Lawrence Fernandes
Karunya University

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This page is a summary of: Study on MACE Gabor Filters, Gabor Wavelets, DCT-Neural Network, Hybrid Spatial Feature Interdependence Matrix, Fusion Techniques for Face Recognition, Recent Patents on Engineering, March 2015, Bentham Science Publishers,
DOI: 10.2174/2210686303666131118220632.
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