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A Similarity-Based Color Descriptor for Face Detection

  • Eyal BraunstainEmail author
  • Isak Gath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9493)

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 contribution 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.

Keywords

Face Image Local Binary Pattern Face Detection Image Patch Color Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Biomedical EngineeringTechnion - Israel Institute of TechnologyHaifaIsrael

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