Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset

Nisha Srinivas, Harleen Atwal, Derek C. Rose, Gayathri Mahalingam, Karl Ricanek, David S. Bolme

Abstract: This paper explores the difficulty of performing automatic demographic prediction on the East Asian population. We introduce the Wild East Asian Face Dataset (WEAFD), a new and unique dataset, to the research community. This dataset consists primarily of labeled face images of individuals from East Asian countries, including Vietnam, Burma, Thailand, China, Korea, Japan, Indonesia, and Malaysia. East Asian Amazon Mechanical Turk annotators were used to label the age, gender and fine grain ethnicity attributes to reduce the impact of the “other-race effect” and improve quality of annotations. We focus on predicting age, gender and fine-grained ethnicity of an individual by providing baseline results using a convolutional neural network (CNN). Fine-grained ethnicity prediction refers to predicting refined categorization of the human population (Chinese, Japanese, Korean, etc.). Performance of two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that improve network optimization by promoting region based feature extraction.