Face Recognition System Is Usually Composed of Detection, Alignment, Representation, Matching and Ot

Face detection. The face detector is used to find the position of the face in the image. If there is a face, it returns the coordinates of the bounding box containing each face. As shown in Figure 3a. 2、 Face alignment. The goal of face alignment is to use a set of reference points located at a fixed position in the image to scale and crop the face image. This process usually needs to use a feature point detector to find a group of face feature points. In the case of simple 2D alignment, that is, to find the best affine transformation suitable for the reference point. Figs. 3b and 3C show two face images aligned using the same set of reference points. More complex 3D alignment algorithms (such as [16]) can also realize the frontal face, that is, adjust the pose of the face to the front forward. 3、 Face representation. In the face representation stage, the pixel value of the face image will be converted into a compact and discriminable feature vector, which is also called template. Ideally, all faces of the same subject should be mapped to similar feature vectors. 4、 Face matching. In the face matching construction module, the two templates will be compared to obtain a similarity score, which gives the possibility that they belong to the same subject.