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Fig. 4.1
Typical
face
detection
steps
based
on
Haar-like
features.
( a )
Haar-like
feature.
( b ) Applied on the candidate. ( c ) Face detected
Pattern (LBP) features. A face can be divided into subsections, each with its own
unique texture quality. Algorithms can search for similar spacial relationships within
the image to potentially locate the face or eyes [ 1 , 11 ].
Local Binary Pattern (LBP) is a simple yet very efficient texture operator which
labels the pixels of an image by thresholding the neighborhood of each pixel
and considers the result as a binary number. Perhaps the most important property
of the LBP operator in real-world applications is its robustness to monotonic
grayscale changes caused, for example, by illumination variations. Another impor-
tant property is its computational simplicity. The LBP operator is a powerful means
for quantitative texture description of an object of interest. The LBP operator is
based on the observation that two-dimensional surface textures can be described
by two complementary measures: local spatial patterns and grayscale contrast.
The basic idea is to form labels for the image pixels by thresholding the
3 3
neighborhood of each pixel with the center value and considering the result as a
binary number. Take a pixel as center and threshold its neighbors against. If the
intensity of the center pixel is greater or equal to its neighbor, then denote it with
1 or 0 if otherwise. We end up with a binary number for each pixel, for example
11001111. With 8 surrounding pixels you'll end up with
2 8 (i.e., 256) possible
combinations, which are called LBP codes. Usually the area of interest is divided
into groups of nonoverlapping
(pixel) neighborhoods. LBP codes have the
ability to capture very fine grained details in an area of interest and have produced
results that were able to compete with state of the art benchmarks for texture
classification when the method was first applied to face recognition [ 1 ]. Soon after
the operator was published, it was noted that a fixed neighborhood fails to encode
details differing in scale. As a result, the operator was extended to use a variable
neighborhood.
3 3
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