SKIN SEGMENTATION USING DIFFERENT INTEGRATED COLOR MODEL APPROACHES FOR FACE DETECTION

Mrunmayee V. Daithankar, Kailash J. Karande

Abstract: This paper presents an efficient skin segmentation algorithm which depends on integration of different color models. Among different low level features such as edge, shape, color, texture; color processing is computationally fast and easy to implement. Color constancy is the ability of human vision system to resolve object colors in a scene independent of the illuminant. Skin color is one of the powerful and effective features to detect the face. To represent skin color different color models are presents which are having luminance and chrominance components like YCbCr, YCgCr and HSI. Though geographically, people of different country has different skin color. This difference lies in their intensity not on their chrominance. Study and Analysis of these color models motivates us to combine the uncommon features of these color models for efficient skin segmentation. As the skin color is highly affected by illumination changes due to luminance i.e. brightness part of color models, we have avoided this component in feature’s combination. Integration of color models contains combination of only chrominance components which will be helpful in handling illumination affects. After skin segmentation, morphological operations such as dilation which adds pixels to the boundary of objects in image and erosion which removes pixels from object’s boundaries are used to get more smooth results. To locate face, face region is detected by checking either 4-point or 8- point connectivity as per requirement and bounded by a rectangle. This process gives us efficient face detection system with 100% face detection rate. This efficient face detection system may results in better final system such as face recognition system, human computer interface system, video surveillance, etc. where face detection is initial and important step.

Keywords: Skin Segmentation, Color Models, Integration of color models, Face Detection, Face localization

DOI: https://doi.org/10.15623/ijret.2014.0308054