Facial ethnicity classification based on boosted local texture and shape descriptions
Huaxiong Ding, Di Huang, Yunhong Wang, Liming Chen · 2013
Ethnicity is a key demographic attribute of human beings and it plays a important role in automatic machine based face analysis, therefore, there has been increasing attention for face based ethnicity classification in recent years. In this paper, we propose a novel method on such an issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the existing ones that only depend on 2D facial images. The proposed method makes use of the Oriented Gradient Maps (OGMs) to highlight local geometry as well as texture variations of entire faces, while further learns a compact set of features which are highly related to the ethnicity property for classification. Experiments are comprehensively carried out on the FRGC v2.0 dataset, and the performance is up to 98.3% to distinguish Asians from non-Asians when 80% samples are used in the training set, demonstrating the effectiveness of the proposed method.