A Fusion Approach for Efficient Human Skin Detection
ABSTRACT:
A
reliable human skin detection method that is adapt-able to different human skin
colors and illumination conditions is essential for better human skin
segmentation. Even though different human skin-color detection solutions have
been successfully applied, they are prone to false skin detection and are not
able to cope with the variety of human skin colors across different ethnic.
Moreover, existing methods require high computational cost. In this paper, we
propose a novel human skin detection approach that combines a smoothed 2-D
histogram and Gaussian model, for automatic human skin detection in color
image(s). In our approach, an eye detector is used to refine the skin model for
a specific person. The proposed approach reduces computational costs as no
training is required, and it improves the accuracy of skin detection despite
wide variation in ethnicity and illumination. To the best of our knowledge,
this is the first method to employ fusion strategy for this purpose.
Qualitative and quantitative results on three standard public datasets and a
comparison with state-of-the-art methods have shown the effectiveness and
robust-ness of the proposed approach.
SYSTEM ANALYSIS:
PROBLEM DEFINITION:
v
A reliable human skin detection method that is
adapt-able to different human skin colors and illumination conditions is
essential for better human skin segmentation.
v
The existing methods require high computational
cost. In this paper, we propose a novel human skin detection approach that
combines a smoothed 2-D histogram and Gaussian model, for automatic human skin
detection in color image(s).
v
In our
approach, an eye detector is used to refine the skin model for a specific
person. The proposed approach reduces computational costs as no training is
required, and it improves the accuracy of skin detection despite wide variation
in ethnicity and illumination.
v
The first method to employ fusion strategy for
this purpose. Qualitative and quantitative results on three standard public
datasets and a comparison with state-of-the-art methods have shown the
effectiveness and robust-ness of the proposed approach.
v Because The
image pixels representation in a suitable color space is the primary step in
skin segmentation in color images. A better survey of different color spaces
for skin-color representation and skin-pixel segmentation methods is given by
Kakumanu et al.
EXISTING SYSTEM:
·
The simplest and commonly used human skin
detection methods is to define a fixed decision boundary for different color
space components. Single or multiple ranges of threshold values for each color
space components are defined and the image pixel values. These predefined
range(s) are selected as skin pixels.
·
In this approach, for any given color space,
skin color occupies a part of such a space, which might be a compact or large
region in the space. These aforementioned solutions that use single fea-tures,
although, successfully applied to human skin detection. They still suffer from
the following.
(1) Low
Accuracy: False skin detection is a common problem when there are a wide
variety of skin colors across different ethnicity, complex backgrounds and high
illumination in image(s).
(2) Luminance-invariant
space: Some robustness may be achieved via the use of luminance invariant color
space, however, such an approach can withstand only changes that skin-color
distribution undergoes within a narrow set of conditions and also degrades the
per-formance.
(3) Require
large training sample: In order to define threshold value(s) for detecting
human skin, most of the state-of-the-art work requires a training stage. One
must under-stand that there are tradeoffs between the size of the training set
and classifier performance.
LIMITATIONS OF EXISTING SYSTEM:
·
The existing methods require high computational
cost, and not introduced in the 2-D histogram methods.
PROPOSED SYSTEM:
·
The proposed approach reduces computational
costs as no training is required, and it improves the accuracy of skin detection
despite wide variation in ethnicity and illumination.
·
To the best of our knowledge, this is the first
method to employ fusion strategy for this purpose. Qualitative and quantitative
results on three standard public datasets and a comparison with state-of-the-art
methods have shown the effectiveness and robust-ness of the proposed approach.
·
A 2-D histogram with smoothed densities and a
Gaussian model are used to model the skin and nonskin distributions,
respectively. Finally, a fusion strategy framework using the product of two
features is employed to perform automatic skin detection.
·
The proposed framework for automatic skin
detection. First, an approach similar to that of Fusel et al. Second, a dynamic
method is employed to calculate the skin threshold value(s) on the detected
face(s) region. Third, two features the 2-D histogram with smoothed densities
and Gaussian models are introduced to represent the skin and non-skin
distributions, respectively. Finally, a fusion framework that uses the product
rule on the two features is employed to obtain better skin detection results.
In this paper, the RGB color space is converted to the LO space to mimic visual
human perception.
ADVANTAGES OF PROPOSED SYSTEM:
The proposed approach reduces computational
costs as no training is required, and it improves the accuracy of skin
detection despite wide variation in ethnicity and illumination. The proposed
method has two advantages in comparison to the state-of-the-art solutions. (1) Our
proposed skin detection method employs an online dynamic threshold approach.
With this, a training stage can be eliminated. (2) We select a fusion strategy
for our skin detector. To the best of our knowledge, this is the first attempt
That employs a fusion strategy to detect skin
in color images.
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