Skin Color Thresholding with OpenCV

This snippet implements common Skin Color Thresholding rules taken from:

  • Nusirwan Anwar bin Abdul Rahman, Kit Chong Wei and John See. RGB-H-CbCr Skin Colour Model for Human Face Detection. (Online available)

Works good for my test data:

 * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
 * Released to public domain under terms of the BSD Simplified license.
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above copyright
 *     notice, this list of conditions and the following disclaimer in the
 *     documentation and/or other materials provided with the distribution.
 *   * Neither the name of the organization nor the names of its contributors
 *     may be used to endorse or promote products derived from this software
 *     without specific prior written permission.
 *   See <>

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace cv;

using std::cout;
using std::endl;

bool R1(int R, int G, int B) {
    bool e1 = (R>95) && (G>40) && (B>20) && ((max(R,max(G,B)) - min(R, min(G,B)))>15) && (abs(R-G)>15) && (R>G) && (R>B);
    bool e2 = (R>220) && (G>210) && (B>170) && (abs(R-G)<=15) && (R>B) && (G>B);
    return (e1||e2);

bool R2(float Y, float Cr, float Cb) {
    bool e3 = Cr <= 1.5862*Cb+20;
    bool e4 = Cr >= 0.3448*Cb+76.2069;
    bool e5 = Cr >= -4.5652*Cb+234.5652;
    bool e6 = Cr <= -1.15*Cb+301.75;
    bool e7 = Cr <= -2.2857*Cb+432.85;
    return e3 && e4 && e5 && e6 && e7;

bool R3(float H, float S, float V) {
    return (H<25) || (H > 230);

Mat GetSkin(Mat const &src) {
    // allocate the result matrix
    Mat dst = src.clone();

    Vec3b cwhite = Vec3b::all(255);
    Vec3b cblack = Vec3b::all(0);

    Mat src_ycrcb, src_hsv;
    // OpenCV scales the YCrCb components, so that they
    // cover the whole value range of [0,255], so there's
    // no need to scale the values:
    cvtColor(src, src_ycrcb, CV_BGR2YCrCb);
    // OpenCV scales the Hue Channel to [0,180] for
    // 8bit images, so make sure we are operating on
    // the full spectrum from [0,360] by using floating
    // point precision:
    src.convertTo(src_hsv, CV_32FC3);
    cvtColor(src_hsv, src_hsv, CV_BGR2HSV);
    // Now scale the values between [0,255]:
    normalize(src_hsv, src_hsv, 0.0, 255.0, NORM_MINMAX, CV_32FC3);

    for(int i = 0; i < src.rows; i++) {
        for(int j = 0; j < src.cols; j++) {

            Vec3b pix_bgr = src.ptr<Vec3b>(i)[j];
            int B = pix_bgr.val[0];
            int G = pix_bgr.val[1];
            int R = pix_bgr.val[2];
            // apply rgb rule
            bool a = R1(R,G,B);

            Vec3b pix_ycrcb = src_ycrcb.ptr<Vec3b>(i)[j];
            int Y = pix_ycrcb.val[0];
            int Cr = pix_ycrcb.val[1];
            int Cb = pix_ycrcb.val[2];
            // apply ycrcb rule
            bool b = R2(Y,Cr,Cb);

            Vec3f pix_hsv = src_hsv.ptr<Vec3f>(i)[j];
            float H = pix_hsv.val[0];
            float S = pix_hsv.val[1];
            float V = pix_hsv.val[2];
            // apply hsv rule
            bool c = R3(H,S,V);

                dst.ptr<Vec3b>(i)[j] = cblack;
    return dst;

int main(int argc, const char *argv[]) {
    // Get filename to the source image:
    if (argc != 2) {
        cout << "usage: " << argv[0] << " <image.ext>" << endl;
    // Load image & get skin proportions:
    Mat image = imread(argv[1]);
    Mat skin = GetSkin(image);

    // Show the results:

    imshow("original", image);
    imshow("skin", skin);

    return 0;

How to contribute

One of the easiest ways to contribute is to participate in discussions. You can also contribute by submitting pull requests.

General feedback and discussions?

Do you have questions or feedback on this article? Please create an issue on the GitHub issue tracker.

Something is wrong or missing?

There may be something wrong or missing in this article. If you want to help fixing it, then please make a Pull Request to this file on GitHub.