A Fast Facial Landmarks Detection and Posture Classification Algorithm

Abstract

In the field of face recognition research, the efficiency of facial landmarks detection and posture classification algorithm is a serious problem. This paper proposes a lightweight network based on convolutional neural network to quickly detect the facial landmarks of human faces. Based on this, the posture is predicted using the relative position of the obtained feature points. The model was tested by the face images captured in various scenes in real life. The accuracy and recall rate of the proposed algorithm were 96.3% and 98.2%, respectively. The test time for a single picture is 0.9ms. The proposed algorithm has less running time and its accuracy and recall rate are similar or even better than other models.