TY - GEN
T1 - Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset
AU - Srinivas, Nisha
AU - Atwal, Harleen
AU - Rose, Derek C.
AU - Mahalingam, Gayathri
AU - Ricanek, Karl
AU - Bolme, David S.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85026303327&partnerID=8YFLogxK
U2 - 10.1109/FG.2017.118
DO - 10.1109/FG.2017.118
M3 - Conference contribution
AN - SCOPUS:85026303327
T3 - Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017
SP - 953
EP - 960
BT - Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017
Y2 - 30 May 2017 through 3 June 2017
ER -
