基于像素的皮肤检测技术

基于像素的皮肤检测技术

介绍一种基于颜色空间的皮肤检测技术，可以检测亚洲人种与白人的皮肤，皮肤检测

人脸识别的基础，也是很多人像识别技术的基础操作，在实际应用中还是非常有用的。

基于像素的皮肤检测主要是寻找正确的颜色空间几何，图像处理中，常见的颜色空间

有如下几种

1.      RGB色彩空间 – R代表单色红，G代表单色绿，B代表单色蓝

2.      HSV色彩空间 – H 代表色彩， S代表饱和度，V代表强度值

3.      YCbCr色彩空间 – 是数字电视的色彩空间

RGB转换为HSV的Java代码如下：

	public static float[] rgbToHSV(int tr, int tg, int tb) {
		float min, max, delta;
		float hue, satur, value;
		min = Math.min(tr, Math.min(tg, tb));
		max = Math.max(tr, Math.max(tg, tb));
		value = max;
		delta = max - min;
		if(max != 0) {
			satur = delta/max;
		} else {
			satur = 0;
			hue = -1;
		}
		
		if(tr == max) {
			hue = (tg - tb)/delta;
		}
		else if(tg == max) {
			hue = 2 + (tb-tr)/delta;
		} else {
			hue = 4 + (tr-tg)/delta;
		}
		hue = hue * 60.0f;
		if(hue <0) {
			hue = hue + 360;
		}
		return new float[]{hue, satur, value};
	}

RGB转换为YCbCr的Java代码如下：

	public static int[] rgbToYcrCb(int tr, int tg, int tb) {
		double sum = tr + tg + tb;
		double r = ((double)tr)/sum;
		double g = ((double)tg)/sum;
		double b = ((double)tb)/sum;
		double y = 65.481 * r + 128.553 * g + 24.966 * b + 16.0d;
		double Cr = -37.7745 * r - 74.1592 * g + 111.9337 * b + 128.0d;
		double Cb = 111.9581 * r -93.7509 * g -18.2072 * b + 128.0d;
		return new int[]{(int)y, (int)Cr, (int)Cb};
	}

(R, G, B) is classified as skin if

R > 95 and G > 40 and B > 20and max{R, G, B} – min{R, G, B} > 15 and |R-G| > 15

and R > G and R > B

实现代码如下：

	public boolean isSkin(int tr, int tg, int tb) {
		int max = Math.max(tr, Math.max(tg, tb));
		int min = Math.min(tr, Math.min(tg, tb));
		int rg = Math.abs(tr - tg);
		if(tr > 95 && tg > 40 && tb > 20 && rg > 15 && 
				(max - min) > 15 && tr > tg && tr > tb) {
			return true;
		} else {
			return false;
		}
	}

一个简单的基于HSV颜色空间的皮肤算法如下：

(H, S, V) will be classified as skin if

H > 0 and H <50 and S > 0.23 andS <0.68

实现代码如下：

	public boolean isSkin(int tr, int tg, int tb) {
		float[] HSV = ColorUtil.rgbToHSV(tr, tg, tb);
		if((HSV[0] > 0.0f && HSV[0] <50.0f ) && (HSV[1] > 0.23f && HSV[1] <0.68f)){
			return true;
		} else {
			return false;
		}
	}

一个简单的基于YCbCr颜色空间的皮肤算法如下：

(Y, Cb, Cr) will be classified as skin if:

Y > 80 and 85<Cb <135 and 135 <Cr <180, and (Y,Cb,Cr)= [0,255] 

对于的Java代码如下:

	public boolean isSkin(int tr, int tg, int tb) {
		int y = (int)(tr * 0.299 + tg * 0.587 + tb * 0.114);
		int Cr = tr - y;
		int Cb = tb - y;
		if(y> 80 && y <255 && Cr > 133 && Cr <173 && 77 基于上述三个算法实现的皮肤检测的效果如下： 
 

 
 
皮肤检测滤镜的源代码如下：
 
 
 
 
package com.process.blur.study;

import java.awt.Color;
import java.awt.image.BufferedImage;

import com.gloomyfish.skin.dection.DefaultSkinDetection;
import com.gloomyfish.skin.dection.FastSkinDetection;
import com.gloomyfish.skin.dection.GaussianSkinDetection;
import com.gloomyfish.skin.dection.HSVSkinDetection;
import com.gloomyfish.skin.dection.ISkinDetection;

public class SkinFilter extends AbstractBufferedImageOp {
	private ISkinDetection skinDetector;
	
	public SkinFilter(int type) {
		if(type == 2) {
			skinDetector = new FastSkinDetection();
		} else if(type == 4) {
			skinDetector = new HSVSkinDetection();
		} else if(type == 8) {
			skinDetector = new GaussianSkinDetection();
		} else {
			skinDetector = new DefaultSkinDetection();
		}
	}

	@Override
	public BufferedImage filter(BufferedImage src, BufferedImage dst) {
		int width = src.getWidth();
        int height = src.getHeight();

        if ( dst == null )
            dst = createCompatibleDestImage( src, null );

        int[] inPixels = new int[width*height];
        int[] outPixels = new int[width*height];
        getRGB( src, 0, 0, width, height, inPixels );
        if(skinDetector instanceof GaussianSkinDetection) {
        	((GaussianSkinDetection)skinDetector).setDispSample(getDispersion(src));
        }
        int index = 0;
        for(int row=0; row> 24) & 0xff;
                tr = (inPixels[index] >> 16) & 0xff;
                tg = (inPixels[index] >> 8) & 0xff;
                tb = inPixels[index] & 0xff;
                if(skinDetector.isSkin(tr, tg, tb)) {
                	outPixels[index] = (ta <<24) | (tr <<16) | (tg <<8) | tb;
                } else {
                	tr = tg = tb = 0;
                	outPixels[index] = (ta <<24) | (tr <<16) | (tg <<8) | tb;
                }               
        	}
        }
        setRGB( dst, 0, 0, width, height, outPixels );
        return dst;
	}
	
	public Color getDispersion(BufferedImage image) {
        // calculate means of pixel  
        int index = 0;
        int height = image.getHeight();
        int width = image.getWidth();
        int[] inPixels = new int[width*height];
        getRGB(image, 0, 0, width, height, inPixels );
        double redSum = 0, greenSum = 0, blueSum = 0;
        Color meanColor = getMean(image);
        double redmeans = meanColor.getRed();
        double greenmeans = meanColor.getGreen();
        double bluemeans = meanColor.getBlue();
        double total = height * width;  
        for(int row=0; row> 24) & 0xff;  
                tr = (inPixels[index] >> 16) & 0xff;  
                tg = (inPixels[index] >> 8) & 0xff;  
                tb = inPixels[index] & 0xff; 
                double rd = (tr - redmeans);
                double gd = (tg - greenmeans);
                double bd = (tb - bluemeans);
                redSum += rd * rd;  
                greenSum += gd * gd;  
                blueSum += bd * bd;  
            }  
        }
        int reddiff = (int)Math.sqrt((redSum / total));
        int greendiff = (int)Math.sqrt((greenSum / total));
        int bluediff = (int)Math.sqrt(blueSum / total);
        System.out.println(" red dispersion value = " + reddiff);
        System.out.println(" green dispersion value = " + greendiff);
        System.out.println(" blue dispersion value = " + bluediff);
		return new Color(reddiff, greendiff, bluediff);
	}
	
	public Color getMean(BufferedImage image) {
        // calculate means of pixel  
        int index = 0;
        int height = image.getHeight();
        int width = image.getWidth();
        int[] inPixels = new int[width*height];
        getRGB(image, 0, 0, width, height, inPixels );
        double redSum = 0, greenSum = 0, blueSum = 0;  
        double total = height * width;  
        for(int row=0; row> 24) & 0xff;  
                tr = (inPixels[index] >> 16) & 0xff;  
                tg = (inPixels[index] >> 8) & 0xff;  
                tb = inPixels[index] & 0xff;  
                redSum += tr;  
                greenSum += tg;  
                blueSum +=tb;  
            }  
        }
        int redmeans = (int)(redSum / total);
        int greenmeans = (int)(greenSum / total);
        int bluemeans = (int)(blueSum / total);
        System.out.println(" red average value = " + redmeans);
        System.out.println(" green average value = " + greenmeans);
        System.out.println(" blue average value = " + bluemeans);
		return new Color(redmeans, greenmeans, bluemeans);
	}
}
 
 
 讨论: 
 
皮肤检测中的后续处理非常重要，可以除去噪声，平滑图像，是皮肤检测的结果
 
 
更加的准确，输出的更容易接受。
 
 

 
 
参考引用：
 
 
《A New Fast Skin Color Detection Technique》 - Tarek M. Mahmoud
 
 
《Improved Automatic Skin Detection in Color Images》 - Filipe Tomaz
 
 
                                               and Tiago Candeias and Hamid Shahbazkia
 
 
《Skin Detection using HSV color space》- unknown author