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partial intensity invariant feature descriptor.
d is the feature descriptor.
For supervised classification, we worked with feature descriptor F2.
The algorithm first extracts local feature descriptor densely.
A feature descriptor that provides scale and direction information is applicable to the proposed method; therefore, we attempted to replace SIFT with another feature descriptor.
Recently, some improvements of feature descriptor parts like restricted SIFT [39] and partial intensity invariant feature descriptor (PIIFD) [20] achieved satisfactory results.
LAI feature descriptor is based on the affine geometry invariant and generated by calculating the proportions of different areas.
It has three major modules: local affine invariant (LAI) feature descriptor, affine transformation estimation and parameters refinement.
A feature descriptor for image classification should have the following essential properties.
In this work, we selected the DAISY feature descriptor to implement and test the proposed method.
The periocular region is extracted from the aligned face image prior to the feature descriptor computation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com