Real-Time Face Tracking Using Local Binary Patterns Histograms Algorithm

Human face detection is an important early stage in automated facial recognition systems. In a single image, the purpose of face detection is to identify all the areas present in the image to find the face area and the non-face area. In image detection, colors have a high sensitivity to changes in light, so to overcome this, RGB image transformation is carried out into a color space whose luminance and chromatic components are separated so that it is enough to use chromatic only for the skin color detection process. Face tracking is carried out with image processing techniques and a complex set of algorithms. The face-tracking process gives the computer the ability to detect the face. The tracking process is carried out using the Local Binary Patterns Histograms algorithm. The Local Binary Patterns Histograms algorithm is one of the object recognition algorithms that divides an image into several parts and then translates them into their representative binary numbers in the form of a histogram. For good tracking results, you must use adequate lighting or bright lighting to get maximum results. Face tracking can be detected correctly with three users at a distance of three meters..