Open AccessDiscriminative common feature subspace learning for age‐invariant face recognition
Author(s) -
Yu YuFeng,
Wang Qiangchang,
Jiang Min
Publication year - 2020
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2019.0104
Subject(s) - discriminative model , pattern recognition (psychology) , subspace topology , artificial intelligence , computer science , facial recognition system , feature (linguistics) , feature learning , feature vector , invariant (physics) , face (sociological concept) , linear subspace , mathematics , social science , philosophy , linguistics , geometry , sociology , mathematical physics
Considering human ageing has a big impact on cross‐age face recognition, and the effect of ageing on face recognition in non‐ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in the original space, and employ the maximum mean discrepancy as the distance measure to compute the distances between the sample means of the different distributions. Then the distance measure is integrated into Fisher criterion to learn a discriminative common feature subspace. The aim is to map the images with different ages to the common subspace, and to construct new feature representation which is robust to age variations and discriminative to different subjects. To evaluate the performance of the proposed method on cross‐age face recognition, the authors construct extensive experiments on CACD and FG‐Net databases. Experimental results show that the proposed method outperforms other subspace based methods and state‐of‐art cross‐age face recognition methods.