Face recognition using binary thresholding for features extraction

The paper deals with a system for the analysis and automated identification of a human face. A face can be recognized when the details of individual features are resolved. The idea is to extract the relative position and other parameters of distinctive features such as eyes, mouth, nose and chin. The overall geometrical configuration of face features can be described by a vector of numerical data representing position and size of main facial features. At first, from sequential images, eye coordinates are extracted by detecting eye winking. The interocular distance and eye position can be used to determine size and position of the areas of search for face features. In these areas binary thresholding is performed, the system modifies the threshold automatically to detect features. To find their coordinates, discontinuities are searched for in the binary image. Experimental results show that the proposed method is robust, valid for numerous kind of facial images in real scenes, works in real time with low hardware requirements and the whole process is conducted automatically