Face Detection on Skin Color Detection. Detecting a person’s skin is an important part of having a good face recognition system.
Skin detection is hard because there are many differences in things like lights and cameras. Plus, people can have different colors of skin (due to their race).
You can use a variety of ways to find out how people look like. For example, you can use the Gaussian model or rule-based methods. We introduce another way that is using an artificial neural network to help figure out what people look like.
Face Detection on Skin Color Detection
Face recognition
Some research in image processing is the ability to identify a face. Humans use this ability all the time. It is important for humans to be able to identify each other and can be used for good or bad purposes. To do this, you need to know what a human’s skin looks like so that you can see faces in an image.
In some pictures, pixels that look like skin are also found on other things. This makes it very hard to see the color of the skin. There is no exact way to tell if you are looking at skin or not.
Skin detection
Choosing a color space is important for skin detection. There are three common choices: RGB, YCbCr, and HSV. They all have their own benefits and drawbacks.
One of the advantages of combining information is that you will keep both useful and useless information.
Neural network
The neural network is a way of identifying things. It has been used in detecting skin only a few times.
The neural network has some settings that can be changed. For example, it can change the number of nodes in the hidden layer, and its initial weights. It will also have different thresholds for each input it receives.
Methods for Skin Color Detection
Here we discuss three methods – Gaussian, rule-based, and neural networks.
Gaussian Methods
This technique finds the skin in an image. It is difficult to find out what color it is because there are many colors, but this technique makes it possible. It changes the RGB image to YCbCr and then picks out skin.
So, the parameters were calculated using training images. For each pixel value, the density function was calculated. The CbCr value is used because the Y component has information that is not related to skin color.
The final output is a picture with black for the skin and white for everything else.
Rule-Based Methods
Researchers have been using skin detection based on rule-based methods as the first step in face detection. They found that it is important to know the colors that are not skin because they can affect the accuracy of the result.
The researchers made the rules and constraints for how to separate skin colors. They did this using Bayesian classification.
This method worked on some images. But it was not good for people with a lot of work or a complicated background.
The neural network
The neural network has been used in skin color detection. It is a type of computer program that can learn from examples. The researchers used YCbCr as the color space and a multilayer perceptron (MLP) neural network to create this skin color detector.
They used two different combining strategies and several combining rules. They found the number of neurons in the hidden layer by using a coarse to the fine search method. Finally, they combined Cb/Cr and Cr features, which produced the best result.
