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Face normalization using multi-scale cortical keypoints
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Orientador(es)
Resumo(s)
Empirical studies concerning face recognition suggest
that faces may be stored in memory by a few canonical representations.
Models of visual perception are based on image
representations in cortical area V1 and beyond, which
contain many cell layers for feature extractions. Simple,
complex and end-stopped cells tuned to different spatial frequencies
(scales) and/or orientations provide input for line,
edge and keypoint detection. This yields a rich, multi-scale
object representation that can be stored in memory in order
to identify objects. The multi-scale, keypoint-based saliency
maps for Focus-of-Attention can be explored to obtain face
detection and normalization, after which face recognition
can be achieved using the line/edge representation. In this
paper, we focus only on face normalization, showing that
multi-scale keypoints can be used to construct canonical
representations of faces in memory.
Descrição
Palavras-chave
Visão computorizada Córtex visual 621.38
Contexto Educativo
Citação
13th Portuguese Conference on Pattern Recognition (RECPAD 2007). - Lisbon, 26 October 2007. - 2 p
Editora
Lisbon