<![CDATA[Face Detection is an important part of digital image processing to determine the location, size and number of faces in an image. Face detection is the initial stage in a facial recognition system that is used for personal identification, human-computer interaction, monitoring systems, criminal law and so on. This study presents face detection with skin color segmentation & template matching methods. The first step is to make a skin color model by transforming into YCbCr and then find the average number of facial skin colors. Next build a Gaussian distribution for the chroma chart which shows the possible skin colors. Adaptive thresholding is used to emphasize skin and non-skin areas presented in binary images. Segmentation of skin areas is done by labeling. Face candidates are obtained from calculating the number of holes in the segmented skin area, calculating the face width-to-height ratio and matching with the face template (template matcing). The centroid of the detected face is calculated and a marker is placed at the centroid of the face in the image. Based on trials with the Matlab 2011 tool with datasets taken from FDDB (Face Detection Data Set and Benchmark), the detection accuracy obtained from trials on 76 images with varied backgrounds and lighting levels reached 81.58%.]]>
Copyrights © 2023
