3D face recognition method against gesture and expression change

In order to improve the facial recognition rate under the change of posture and expression, combined with the ability of local plane distance (DLP) to judge the convexity of local curved surface, a face recognition method based on face equidistant invariant representation was proposed. Firstly, several operations such as distance constraint, location constraint and transformation were conducted on the depth image captured by the deep camera to get the clean and complete 3D face; then the nose tip was determined by the DLP value of every point on the 3D face, and the nasal root was determined by the clustering idea; secondly, the improved fast propulsion algorithm was used to calculate the geodesic distance matrix of face, then the threshold value was set and the effective face area was cut out; finally, the high-order moment feature of the effective face area was calculated as the feature vector of face for matching. The experimental results show that the recognition rate of this algorithm is close to 97% for different databases. Compared with the face recognition algorithms based on contour features and Gabor features, the recognition rate of this algorithm is increased by 14.1% and 8.3%, respectively, while having a high computing efficiency.