Attribute estimation using multi-CNNs from hand images

Yi Chun Lin, Yusei Suzuki, Hiroya Kawai, Koichi Ito, Hwann Tzong Chen, Takafumi Aoki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ241-244
ページ数4
ISBN(電子版)9781728132488
DOI
出版ステータスPublished - 2019 11
イベント2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
継続期間: 2019 11 182019 11 21

出版物シリーズ

名前2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
国/地域China
CityLanzhou
Period19/11/1819/11/21

ASJC Scopus subject areas

  • 情報システム

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