Journal Article10.1111/J.1467-8659.2008.01158.X
Image-based Aging Using Evolutionary Computing
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TL;DR: This paper uses a data‐driven framework for automatic image‐based facial transformation in conjunction with a database of facial images and builds a novel parameterized model for encoding age‐transformation in addition with the traditional model for face description.
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Abstract: Aging has considerable visual effects on the human face and is difficult to simulate using a universally-applicable global model. In this paper, we focus on the hypothesis that the patterns of age progression (and regression) are related to the face concerned, as the latter implicitly captures the characteristics of gender, ethnic origin, and age group, as well as possibly the person-specific development patterns of the individual. We use a data-driven framework for automatic image-based facial transformation in conjunction with a database of facial images. We build a novel parameterized model for encoding age-transformation in addition with the traditional model for face description. We utilize evolutionary computing to learn the relationship between the two models. To support this work, we also developed a new image warping algorithm based on non-uniform radial basis functions (NURBFs). Evolutionary computing was also used to handle the large parameter space associated with NURBFs. In comparison with several different methods, it consistently provides the best results against the ground truth.
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Citations
•Proceedings Article
A morphable model for the synthesis of 3D faces
Matthew Turk
- 01 Jan 1999
TL;DR: A new technique for modeling textured 3D faces by transforming the shape and texture of the examples into a vector space representation, which regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance.
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•Posted Content
Personalized Age Progression with Aging Dictionary
TL;DR: In this paper, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process.
130
An inverse problem approach for automatically adjusting the parameters for rendering clouds using photographs
Yoshinori Dobashi,Wataru Iwasaki,Ayumi Ono,Tsuyoshi Yamamoto,Yonghao Yue,Tomoyuki Nishita +5 more
- 01 Nov 2012
TL;DR: This paper proposes a method for addressing an inverse rendering problem: given a non-uniform synthetic cloud density distribution, the parameters for rendering the synthetic clouds are estimated using photographs of real clouds using genetic algorithms.
Kinship-Guided Age Progression
TL;DR: This work presents an efficient and effective Kinship-Guided Age Progression (KinGAP) approach for an individual, which can automatically generate personalized aging images by leveraging kinship, or more specifically, with guidance of the senior kinship face.
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Automatic Generation of 3D Caricatures Based on Artistic Deformation Styles
L Clarke,Min Chen,Benjamin Mora +2 more
TL;DR: This paper introduced a pseudo stress-strain model to encode the parameters of an artistic deformation style using “virtual” physical and material properties and developed a software system for performing the caricaturistic deformation in 3D which eliminates the undesirable artifacts in 2D caricaturization.
30
References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
58.6K
•Book
Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
- 01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
•Book
Genetic Algorithms
David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
17.1K
Active appearance models
Abstract: We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
Active Appearance Models
Timothy F. Cootes,G.J. Edwards,Christopher J. Taylor +2 more
- 02 Jun 1998
TL;DR: A novel method of interpreting images using an Active Appearance Model (AAM), a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example.