TY - GEN
T1 - Attribute estimation using multi-CNNs from hand images
AU - Lin, Yi Chun
AU - Suzuki, Yusei
AU - Kawai, Hiroya
AU - Ito, Koichi
AU - Chen, Hwann Tzong
AU - Aoki, Takafumi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - The human hand is one of the primary biometric traits in person authentication. A hand image also includes a lot of attribute information such as gender, age, skin color, accessory, and etc. Most conventional methods for hand-based biometric recognition rely on one distinctive attribute like palmprint and fingerprint. The other attributes as gender, age, skin color and accessory known as soft biometrics are expected to help identify individuals but are rarely used for identification. This paper proposes an attribute estimation method using multi-convolutional neural network (CNN) from hand images. We specially design new multi-CNN architectures dedicated to estimating multiple attributes from hand images. We train and test our models using 11K Hands, which consists of more than 10, 000 images with 7 attributes and ID. The experimental results demonstrate that the proposed method exhibits the efficient performance on attribute estimation.
AB - The human hand is one of the primary biometric traits in person authentication. A hand image also includes a lot of attribute information such as gender, age, skin color, accessory, and etc. Most conventional methods for hand-based biometric recognition rely on one distinctive attribute like palmprint and fingerprint. The other attributes as gender, age, skin color and accessory known as soft biometrics are expected to help identify individuals but are rarely used for identification. This paper proposes an attribute estimation method using multi-convolutional neural network (CNN) from hand images. We specially design new multi-CNN architectures dedicated to estimating multiple attributes from hand images. We train and test our models using 11K Hands, which consists of more than 10, 000 images with 7 attributes and ID. The experimental results demonstrate that the proposed method exhibits the efficient performance on attribute estimation.
UR - http://www.scopus.com/inward/record.url?scp=85082392204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082392204&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC47483.2019.9023260
DO - 10.1109/APSIPAASC47483.2019.9023260
M3 - Conference contribution
AN - SCOPUS:85082392204
T3 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
SP - 241
EP - 244
BT - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Y2 - 18 November 2019 through 21 November 2019
ER -