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Robust Skin Area Detection Method in Color Distorted Images

색 왜곡 영상에서의 강건한 피부영역 탐지 방법

  • Hwang, Daedong (Research Center, Bosung Tech.,Inc.) ;
  • Lee, Keunsoo (Dept. of Computer Science & Engineering (Computer System Institute), Hankyong National University)
  • 황대동 (보성테크(주) 기술연구소) ;
  • 이근수 (한경대학교 컴퓨터공학과(컴퓨터 시스템 연구소))
  • Received : 2017.05.29
  • Accepted : 2017.07.07
  • Published : 2017.07.31

Abstract

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

실시간 인체 검출에 대한 관심이 높아짐에 따라 피부색을 통한 인체 검출에 대한 연구가 활발히 진행되고 있다. 하지만 대다수 기존 피부 탐지 방법은 정적인 피부색 모델을 이용하기 때문에 색 왜곡이 발생한 영상에서 낮은 탐지율을 보인다. 이러한 문제를 해결하기 위해 본 논문에서는 경사도 맵과 채도, YCbCr 공간의 Cb, Cr 요소를 퍼지로 분류하는 방법을 사용하여 피부영역을 탐지하는 기법을 제시한다. 제안하는 방법의 기본적인 절차는 경사도 맵 생성, 채도 맵 생성, CbCr 맵 생성, 퍼지 분류, 피부영역 이진화 순이다. 이 방법은 색상 이외의 특징을 이용하여 조명, 인종, 나이, 개인차 등에 상관없이 강건하게 피부를 탐지하는 것에 중점을 두고 있다. 색상 이외의 피부 특징은 비피부영역과의 경계가 모호하여 구분이 명확하지 않다. 이를 해결하기 위해 경사도, 채도와 색상 특징간의 관계를 소속함수로 정의하고 이를 이용하여 108가지의 퍼지 규칙을 생성하여 피부영역을 탐지한다. 제안한 방법의 검출 정확도는 86.35%로 기존 방법보다 2~5 % 우수함을 확인하였다.

Keywords

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