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

Conference · Sun Jan 01 00:00:00 EST 2017

OSTI ID:1361352

Srinivas, Nisha ^[1]; Rose, Derek C ^[1]; Bolme, David S ^[1]; Mahalingam, Gayathri ^[1]; Atwal, Harleen ^[1]; Ricanek, Karl ^[1]

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1. ORNL

This paper examines the difficulty associated with performing machine-based automatic demographic prediction on a sub-population of Asian faces. 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 turk annotators were uniquely used to judge the age 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 with a convolutional neural network (CNN). Finegrained ethnicity prediction refers to predicting ethnicity of an individual by country or sub-region (Chinese, Japanese, Korean, etc.) of the East Asian continent. Performance for two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that ease network optimization by promoting region based feature extraction.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: Work for Others (WFO)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1361352
• Resource Relation: Conference: International Workshop on Biometrics in the Wild, Washington DC, DC, USA, 20170530, 20170603
• Country of Publication: United States
• Language: English