Discriminative aging subspace learning for age estimation

Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analy...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 26; číslo 18; s. 9189 - 9198
Hlavní autoři: Sawant, Manisha, Bhurchandi, Kishor M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Témata:
Artificial Intelligence
Computational Intelligence
Control
Data Analytics and Machine Learning
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Regression
Aging manifold
Age estimation
Hidden factor analysis (HFA)
ISSN:1432-7643, 1433-7479
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Shrnutí:Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject’s age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07333-z