Authors:
Eyal Braunstain
and
Isak Gath
Affiliation:
Technion - Israel Institute of Technology, Israel
Keyword(s):
Face detection, Object recognition, Descriptors, Color.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
Abstract:
Most state-of-the-art approaches to object and face detection rely on intensity information and ignore color
information, as it usually exhibits variations due to illumination changes and shadows, and due to the lower
spatial resolution in color channels than in the intensity image. We propose a new color descriptor, derived
from a variant of Local Binary Patterns, designed to achieve invariance to monotonic changes in chroma. The
descriptor is produced by histograms of encoded color texture similarity measures of small radially-distributed
patches. As it is based on similarities of local patches, we expect the descriptor to exhibit a high degree of
invariance to local appearance and pose changes. We demonstrate empirically by simulation the invariance
of the descriptor to photometric variations, i.e. illumination changes and image noise, geometric variations,
i.e. face pose and camera viewpoint, and discriminative power in a face detection setting. Lastly, we show
that the contribut
ion of the presented descriptor to face detection performance is significant and superior to
several other color descriptors, which are in use for object detection. This color descriptor can be applied in
color-based object detection and recognition tasks.
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