IEICE Trans - Gender Attribute Mining with Hand-Dorsa Vein Image Based on Unsupervised Sparse Feature Learning

Gender Attribute Mining with Hand-Dorsa Vein Image Based on Unsupervised Sparse Feature Learning

Jun WANG
Guoqing WANG
Zaiyu PAN

Publication
IEICE TRANSACTIONS on Information and Systems Vol.E101-D No.1 pp.257-260
Publication Date: 2018/01/01
Publicized: 2017/10/12
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8098
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
Keyword:
gender recognition, unsupervised sparse feature learning, data reconstruction,

Full Text: PDF(591.5KB)>>

Summary:
Gender classification with hand-dorsa vein information, a new soft biometric trait, is solved with the proposed unsupervised sparse feature learning model, state-of-the-art accuracy demonstrates the effectiveness of the proposed model. Besides, we also argue that the proposed data reconstruction model is also applicable to age estimation when comprehensive database differing in age is accessible.

open access publishing via