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Minimal Gabor Filter Bank for Face Features Localization


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DOI: https://doi.org/10.15866/irecos.v9i12.4695

Abstract


We propose an optimized Gabor filter bank for facial features localization. Our analysis shows that the responses to filters with selected parameters offer significantly higher values of certain statistics. The highest values of standard deviation and energy confirm their relevance as descriptors for classification. Compared to the banks with different numbers of filters, the proposed bank of 8 filters offers the best classification sensitivity using MLP.
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Keywords


Gabor Filter Bank; Texture; Face Features Localization; MLP

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References


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