Bigailab-4race-50K: Race Classification with a New Benchmark Dataset

Abstract

Face analysis is the process of extracting useful information such as gender, age and race from a face image. In this study, we look at ways of closing the gap between the capabilities of automatic facial recognition and race classification methods. For this purpose, we take the following two steps. (1) We offer a public race dataset consisting of labeled images to overcome the challenges of real-world race estimation tasks. (2) We present a benchmark study to find race of a human face using a pre-trained Convolutional Neural Network (CNN) model on the large-scale face dataset. We are able to achieve a remarkable accuracy of 97.6% in race classification task.