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



Título del documento: Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction
Revista: Journal of applied research and technology
Base de datos: PERIÓDICA
Número de sistema: 000384084
ISSN: 1665-6423
Autores: 1
1
1
1
Instituciones: 1COMSATS Institute of Information Technology, Department of Computer Sciences, Wah Cantt. Pakistán
Año:
Periodo: Jun
Volumen: 13
Número: 3
Paginación: 402-408
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés Feature transformation and key-point identification 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
Disciplinas: Ciencias de la computación
Palabras clave: Procesamiento de datos,
Procesamiento de imágenes,
Transformación en características invariantes a la escala,
Imágenes hexagonales,
Reconocimiento de rostros
Keyword: Computer science,
Data processing,
Image processing,
Scale invariant feature transform,
Hexagonal images,
Face recognition
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