Given the non-intrusive nature and broad surveillance application of face recognition

this technology has drawn considerable attention in the fields of pattern recognition and computer vision. However

expression variation is one of the main challenges in 3D face recognition because the geometric shape of a face changes drastically under expression variation. For instance

an open mouth can significantly change the topology of the facial surface

which can degrade the performance of a 3D face recognition system. To handle facial expressions

a novel 3D face recognition method based on facial profiles is proposed. First

the pose of a cropped face is automatically corrected on the basis of principal component analysis

and all facial scans are transformed into a uniform pose coordinate system.A set of vertical facial profiles in the upper half face region is then extracted to represent a 3D facial scan.Hence

the shapes of two facial scans can be matched by fitting the shapes of the corresponding facial profiles. Open curve analysis algorithm is applied to calculate the geodesic distance between a pair of facial profiles extracted from different facial scans.The geodesic distance is used as a similarity measure. Finally

two facial scans can be matched by using the weighted sum of all levels of the corresponding geodesic distance. One of the large stavailable public domain 2D and 3D human face datasets is the Face Recognition Grand Challenge (FRGC)v2.0

which has been widely used in the literature. Two experiments are conducted using the FRGC v2.0 dataset: recognition and expression robustness experiments. In the recognition experiment

the earliest neutral 3D facial scan of every individual is selected to create a gallery of 466 facial scans

and the rest are used as probes. We test three dataset partition methods that are commonly used in existing 3D face recognition systems

which also use FRGC v2.0 as the testing dataset(i.e.

non-neutral vs. neutral

all vs. neutral

and neutral vs. neutral). The Rank-1 recognition rates of our proposed approach in the cases of non-neutral vs. neutral

all vs. neutral

and neutral vs. neutral are 95.2%

97.1%

and 98%

respectively. In the expression-robustness experiment

we consider the gallery in the recognition experiment

and 816 facial scans with an open mouth from the FRGC v2.0 dataset are used as the testing set for face recognition. When the facial profiles are extracted from all the facial regions as features

the Rank-1 recognition rate is 82.8%

whereas that of our proposed method is 93.5%. Achieving high accuracy in the presence of expression variation is one of the most challenging aspects of 3D face recognition.To address this problem

a 3D face recognition method based on facial profiles is proposed.A set of vertical facial profiles are then extracted to represent facial surface. Given that these facial profiles are extracted from the semi-rigid region of a face

our proposed approach weakens the adverse effects caused by facial expression

especially large facial expression deformation

and consequently improves the efficiency of face matching. Experiments are performed using the FRGC v2.0 dataset to demonstrate the effectiveness of our algorithm. Results confirm the expression-robustness of the proposed method.