An efficient image-mosaicing method based on multifeature matching
Machine Vision and Applications
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We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semi-profile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a terrain transform that exploits neighborhood properties to determine the transformation relating the 2 images. Multiresolution splining is then used to blend the side profiles with the frontal image thereby generating a composite face image of the user. A local binary pattern matching algorithm is used to compare an input face image with the template face mosaic. Experiments conducted on a small dataset of 27 users indicate that face mosaicing as described in this paper offers significant benefits by accounting for the pose variations commonly observed in face images.