Comparison of Different Face-Detection Algorithm

It is based on finding faces from an image or video by using new techniques such as single reading to get better face recognition and create a student learning guide. Image features are visualized based on subtle visual features such as color, size, edges, movement etc. The face detection algorithm is very specific to the type of problem and cannot be guaranteed to work unless it is used and results are obtained. We followed a multi-faceted algorithm approach, which is a collection of simple rejection algorithms. In the development of final algorithms many different strategies have been attempted. The first step is skin separation, which is okay by rejecting too much detail. So this forms the first step of the final algorithm again. Neural networks have also been used. Seeing someones face from the picture. Face detection can be done in the following ways: Geometry: Based on the geometric relationships between facial features, or in other words the spatial configuration of facial features. That means that the basic geometric features of the face such as eyes, nose and mouth are the first to be identified and the face is classified on the basis of geometrical distance that does not differ between the features. Photometric stereo: It is used to obtain the composition of an object from many images taken under different light conditions. The structure of the find is described by a map that looks great, formed by a collection of common places.