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3 The face is illuminated by the nonuniform illumination field and the white balancing partially fails. The color appearance of the face varies at different parts of the light field
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This chapter deals with the role of color in facial image analysis such as face detection and recognition. First, we introduce
the use of color information in computer vision in general and in the field of facial image analysis in particular. Then,
we give an introduction to color formation and discuss the effect of illumination on color appearance...
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Citations
... Although individual lemurs do show variation in pelage/skin coloration, we disregard color information from the images. In human face recognition studies, skin color is known to be sensitive to illumination conditions and therefore is not considered to be a reliable attribute [57,58]. ...
Background
Long-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, population-level studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs. ResultsLemurFaceID was developed using patch-wise Multiscale Local Binary Pattern features and modified facial image normalization techniques to reduce the effects of facial hair and variation in ambient lighting on identification. We trained and tested our system using images from wild red-bellied lemurs (Eulemur rubriventer) collected in Ranomafana National Park, Madagascar. Across 100 trials, with different partitions of training and test sets, we demonstrate that the LemurFaceID can achieve 98.7% ± 1.81% accuracy (using 2-query image fusion) in correctly identifying individual lemurs. Conclusions
Our results suggest that human facial recognition techniques can be modified for identification of individual lemurs based on variation in facial patterns. LemurFaceID was able to identify individual lemurs based on photographs of wild individuals with a relatively high degree of accuracy. This technology would remove many limitations of traditional methods for individual identification. Once optimized, our system can facilitate long-term research of known individuals by providing a rapid, cost-effective, and accurate method for individual identification.
... The color of human skin has different appearances, different races; even the same person under different light sources or using different types of clothes, however, the color is relatively narrow in a color space [3] [8]. Under steady illumination and uniform color signal remains strong against geometric, such as scale and rotation changes [9]. The skin color detection plays an important role in a wide range of image processing applications, as tracking and face detection, gesture analysis, and is used as a preliminary step in the recognition of faces. ...
The face segmentation is an important step for face recognition because it is not only locates the face but also get a correct segmentation for a fuller region possible. In this paper an application of the viscous opening along the skin color for segmentation and obtaining the limit of facial contour is presented. The proposal consists of two stages: the first one use the classification of pixels based on skin color given an input image to find the region of interest; in the second stage, the viscous opening is used to generate two markers of the resulting image from the previous stage, with the objective of maintaining only the facial area, avoiding the neck area.
