Ethnicity classification by the 3D Discrete Landmarks Model measure in Kendall shape space

Abstract

Knowledge of human ethnicity constitutes important biometric information. An automated ethnicity classification is a good first step in facial analysis. However, most ethnicity classification methods require a complex feature extraction and model training process. We propose a novel ethnicity classification method based on the analysis of facial landmarks in Kendall shape space. Facial features with different relative positions have a close relationship with ethnicity. Facial landmarks can represent positions of facial features. We build a Discrete Landmarks Model (DLM) based on facial landmarks and construct an ethnicity classification model based on the DLM analysis. The clear advantages of our method are that it is fully automated; requires no complex data preprocessing, feature extraction or a complex training process; results in a fast and accurate classification process. We estimate the effectiveness of our method experimentally, using public databases such as Texas3D, FRGC2.0, BU-3DFE and BU-4DFE. On average, our method can achieve a 95% ethnicity classification rate with each classification attempt in 2.0 s.