Mr. Sinan A. Naji Profile

Sinan A. Naji

at Univ of Malaya

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Publications (1)

Proceedings Article | 26 February 2010 Paper

Multi-skin color clustering models for face detection

Roziati Zainuddin, Sinan Naji

Proceedings Volume 7546, 75460S (2010) https://doi.org/10.1117/12.853473

KEYWORDS: Skin, Image segmentation, Facial recognition systems, RGB color model, 3D modeling, Light sources and illumination, Image processing, Databases, Image classification, Cameras

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Automatic face detection in colored images is closely related to face recognition systems, as a preliminary critical required step, where it is necessary to search for the precise face location. We propose a reliable approach for skin color segmentation to detect human face in colored images under unconstrained scene conditions that overcoming the sensitivity to the variation in face size, pose, location, lighting conditions, and complex background. Our approach is based on building multi skin color clustering models using HSV color space, multi-level segmentation, and rule-based classifier. We proposed to use four skin color clustering models instead of single skin clustering model, namely: standard-skin model, shadow-skin model, light-skin model, high-red-skin model. We made an independent skin color clustering models by converting 3-D color space to 2-D without losing color information in order to find the classification boundaries for each skin color pattern class in 2-D. Once we find the classification boundaries, we process the input image with the first-level skin-color segmentation to produce four layers; each layer reflecting its skin-color clustering model. Then an iterative rule-based region grow is performed to create one solid region of interest which is presumed to be a face candidate region that will be passed to the second-level segmentation. In this approach we combine pixel-based segmentation and region-based segmentation using the four skin layers. We also propose skin-color correction (skin lighting) at shadow-skin layer to improve detection rate. In the second-level segmentation we use gray scale to segment the face candidate region into the most significant features using thresholding. Next step is to compute the X-Y-reliefs to locate the accurate position of facial features in each face candidate region and match it with our geometrical knowledge in order to classify the face candidate region to a face or non-face region. We present experimental results of our implementation and demonstrate the feasibility of our approach to be general purpose skin color segmentation for face detection problem.

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