Research On SDM Face Pose Estimation Based On Multi-template Matching

With the increasing number of people-centered computing applications,the importance of face analysis research is increasing.Estimating head pose from face images has gradually become an important problem in the applications of 3D face modeling,driver monitoring security system,intelligent man-machine interaction and so on.In addition,it is necessary to deal with head pose change in the solution of face recognition problem.According to the background and significance of face pose estimation,this paper presents a SDM face pose estimation method based on multi-template matching.This paper mainly includes four parts:face detection,face feature point location,head pose estimation and head pose parameter measurement.The work is as follows:First,face detection method.This paper uses face detection method based on Adaboost algorithm and combines Haar features to get strong classifier by training weak classifier,then get cascade classifier for strong classifier cascade,then realize face detection,and improve the case with multiple face frames produced in the detection process.Face detection is the first step in the preprocessing of head pose estimation.Secondly,the location method of facial feature points.This paper adopts the face feature point localization method based on SDM algorithm.Aiming at the large pose deflection,the feature point localization effect is not good,the multi-template matching sdm algorithm is proposed.the face is divided into 5 categories according to the angle of deflection.Different poses correspond to different models,which are processed by LBP features and random forest methods,and verified in experiments.Face feature point localization is a key step in head pose estimation,providing feature extraction for head pose preprocessing.Finally,face pose estimation.This paper uses the face pose estimation method of POSIT algorithm to calibrate the camera by OpenCV,obtain the camera internal parameters,and solve the parameters of the head pose with the face feature point,3D head model as input parameters.