Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction

Feature transformation and key-point identifi is the solution to many local feature descriptors. One among such descriptor is the Scale Invariant Feature Transform (SIFT). A small effort has been made for designing a hexagonal sampled SIFT feature descriptor with its applicability in face recognition tasks. Instead of using SIFT on square image coordinates, the proposed work makes use of hexagonal converted image pixels and processing is applied on hexagonal coordinate system. The reason of using the hexagonal image coordinates is that it gives sharp edge response and highlights low contrast regions on the face. This characteristic allows SIFT descriptor to mark distinctive facial features, which were previously discarded by original SIFT descriptor. Furthermore, Fisher Canonical Correlation Analysis based discriminate procedure is outlined to give a more precise classification results. Experiments performed on renowned datasets revealed better performances in terms of feature extraction in robust conditions.