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Proceedings ArticleDOI

Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features

TLDR
Through manifold analysis of face pictures, a novel age estimation framework is developed to find a sufficient embedding space and model the low-dimensional manifold data with a multiple linear regression function.
Abstract
Extensive recent studies on human faces reveal significant potential applications of automatic age estimation via face image analysis. Due to the temporal features of age progression, aging face images display sequential pattern of low-dimensional distribution. Through manifold analysis of face pictures, we developed a novel age estimation framework. The manifold learning methods are applied to find a sufficient embedding space and model the low-dimensional manifold data with a multiple linear regression function. Experimental results on a large size age database demonstrate the effectiveness of the framework. To our best knowledge, this is the first work involving the manifold ways of age estimation.

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Citations
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Journal ArticleDOI

Age Synthesis and Estimation via Faces: A Survey

TL;DR: The complete state-of-the-art techniques in the face image-based age synthesis and estimation topics are surveyed, including existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are provided.
Journal ArticleDOI

Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Proceedings ArticleDOI

Human age estimation using bio-inspired features

TL;DR: This work investigates the biologically inspired features (BIF) for human age estimation from faces with significant improvements in age estimation accuracy over the state-of-the-art methods and proposes a new operator “STD” to encode the aging subtlety on faces.
Proceedings ArticleDOI

AgeDB: The First Manually Collected, In-the-Wild Age Database

TL;DR: This paper presents the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels, which renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in the wild".
Journal ArticleDOI

Facial Age Estimation by Learning from Label Distributions

TL;DR: Li et al. as mentioned in this paper proposed a label distribution approach for facial age estimation, which covers a certain number of class labels, representing the degree that each label describes the instance, and two algorithms, named IIS-LLD and CPNN, are proposed to learn from such label distributions.
References
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Journal ArticleDOI

Nonlinear dimensionality reduction by locally linear embedding.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Proceedings Article

Locality Preserving Projections

TL;DR: These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
Book

Applied Linear Regression

TL;DR: In this paper, the authors present a method to estimate the least squares of a scatterplot matrix using a simple linear regression model, and compare it with the mean function of the scatterplot matrices.
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