3D Face Recognition Under Uncontrolled Conditions

Face recognition has been a key technology in the field of computer image processing because it is natural,friendly,and non-disturbing.In the last two decades,2D face recognition research has made great progress.Most 2D face recognition algorithms can achieve reliable recognition performance under controlled conditions.However,the development of 2D face recognition is restricted because it is sensitive to illumination,pose,and expression.With the rapid development of the 3D scanning techniques,3D face recognition has received growing attention in recent years.A large number of studies on 3d face recognition have emerged.In this paper,we mainly study the problem of 3d face recognition under uncontrolled conditions and give the effective solution.The main research content and contributions are as follows:(1)A method for 3D facial landmark localization is presented.The method is insensitive to pose and expression.Candidate landmarks are detected using HK curvature analysis.According to the priori knowledge on facial shape,a facial geometrical structure-based classification strategy is proposed to subdivide the candidate landmarks.Landmark localization is obtained by matching candidate landmarks with a Facial Landmark Model(FLM).The landmark localization accuracy of our method is first experimented on the CASIA dataset.Then,our method is compared with the state-of-the-art methods on the UND/FRGC v2.0 dataset.Experimental results confirm that our method achieves high accuracy and robustness both to large pose and expression variations.(2)A new method for expression-invariant 3D face recognition is proposed.A 3D face is partitioned into a set of iso-geodesic stripes and the spatial relationships of stripes are described by 3D Weighted Walkthrough(3DWW)and centroid distance.Moreover,the way of similarity measure is given.Experiments are performed on the CASIA dataset and the FRGC v2.0 dataset.The results show that the proposed method has advantages on recognition performance despite large expression variations.(3)A new method for pose-invariant 3D face recognition is proposed to handle significant pose variations.It uses an automatic landmark detector to estimate pose for each facial scan.Subsequently,a reference model is registered to the scan.By using the half face matching,it can seamlessly handle frontal and side facial scans.Experiments carried out on the Bosphorus and UND/FRGC v2.0 databases show that the proposed method has high accuracy and robustness to pose variations.(4)A 3D face recognition approach based on keypoints and local descriptors is presented.Firstly,keypoints on the facial scan are detected as mean curvature extrema in scale space.Then,a Mesh Edge Point Filter is proposed to remove edge keypoints.A descriptor vector which consists of concatenated histograms of geometric shapes and shape indices is designed to describe local shapes of keypoints.Finally,the identity of a probe scan is determined by using a multitask SRC,Results from experiments on the Bosphorus database demonstrate the effectiveness of the proposed approach in the presence of large expressions variations,large pose variations,and occlusions.