Gender and ethnicity classification of the 3D nose region based on scaling invariant harmonic wave kernel signature

In 3D face analysis research, automated classification to recognize gender and ethnicity has received an increasing amount of attention in recent years. Feature extraction and feature calculation have a fundamental role in the process of classification construction. In particular, the challenge of 3D low-quality face data, including inconsistent mesh numbers or holes, makes it difficult to extract and calculate facial features. To overcome this challenge, we propose a shape descriptor Scaling invariant harmonic wave kernel signature (SIHWKS) that is robust to scaling, topology and sampling and can effectively describe the global and local properties of 3D shapes simultaneously by involving two energy parameters. We extract a local nose region in the center of the face using isogeodesic stripes replacing full facial information, which has a lower probability of occlusion and lower calculation complexity. Actually, the local nose region is constrained by the skull so that it has high distinction gender and ethnicity property and stability property that are robust to 3D facial expression for gender and ethnicity classification. Compared with gender and ethnicity classification based on 2D deep-learning methods influenced by texture information, the proposed method does not require complex processes for model training and only considers the geometric information of the 3D nose region. In addition, to estimate the effectiveness of our point descriptor SIHWKS for gender and ethnicity classification, we compare our SIHWKS with four existing descriptors – global point signature (GPS), heat kernel signature (HKS), wave kernel signature (WKS) and harmonic wave kernel signature (HWKS) – on four databases, namely, FRGC2.0, Bosphorus3D, Facewarehouse and Asian Mongolian craniofacial. Finally, we perform experiments comparing our method with other recent existing classification methods. The experimental results show that our proposed method can achieve a higher accuracy rate for gender and ethnicity classification.