Compared with other biometric recognition methods

face recognition is friendlier

more natural

and less interferential to users. Hence

face recognition has numerous applications in various fields. However

the 3D shape of the human face is a nonrigid free-form surface

which causes face region distortion under expression variations

particularly in the mouth region. Therefore

3D face recognition is easily affected by facial expression variations. Laughing and acting surprised make the face produce a hole in the mouth region and consequently change the topology of the facial model. This study proposes a 3D face recognition method based on facial fiducial points to avoid the effect of different facial expressions. The active shape model algorithm

which is usually used for 2D images

is applied to roughly detect facial fiducial points in depth images. The shape index is used to accurately locate fiducial points in 3D point clouds. A series of iso-geodesic contours from the landmark (located in the middle of nose tip and nose root) is then extracted to represent the facial shape and avoid the mouth region. Procrustean features (distances and angles) defined by pose-invariant curves are selected as the final recognition features. The classification results of each geodesic contour are compared and combined at the decision level as the final results. FRGC V2.0 is a large public face dataset. Experiments on the detection and recognition of facial fiducial points are conducted using the FRGC V2.0 dataset. In the detection experiment

face models in fall 2003 are selected as the training set

and 150 scans acquired in spring 2004 are selected as the testing set. Seven fiducial points

including eye corners

nose tip

and mouth corners

are manually located in the testing set. The accuracy of the seven detected fiducial points is measured through the Euclidean distance between the manually detected fiducial points and the corresponding automatic points. Each of the seven points is accurately detected

and the mean value of the positional error is less than 2.36 mm. In the recognition experiment

424 scans from 60 subjects in the FRGC V2.0 database are randomly selected. Each iso-geodesic contour with procrustean features (distances and angles) is tested

and the classification results of the eight iso-geodesic contours are weighted in a decision-level fusion. The final Rank-1 recognition rate is 98.35%. Expression variation is an important research direction for 3D face recognition. This study proposes a novel method for locating fiducial points in 3D point clouds. Combined with the depth images and 3D point clouds

seven fiducial points are completely

automatically

rapidly

and accurately detected. A series of facial contours based on these points is extracted to represent the facial surface. The effect of expression variations on recognition is decreased because the iso-geodesic contours are located in approximate rigid regions. Overall

the proposed method is valid and robust in the presence of pose and expression variations.