19 September 2017 Face aging effect simulation model based on multilayer representation and shearlet transform
Author Affiliations +
Abstract
In order to extract detailed facial features, we build a face aging effect simulation model based on multilayer representation and shearlet transform. The face is divided into three layers: the global layer of the face, the local features layer, and texture layer, which separately establishes the aging model. First, the training samples are classified according to different age groups, and we use active appearance model (AAM) at the global level to obtain facial features. The regression equations of shape and texture with age are obtained by fitting the support vector machine regression, which is based on the radial basis function. We use AAM to simulate the aging of facial organs. Then, for the texture detail layer, we acquire the significant high-frequency characteristic components of the face by using the multiscale shearlet transform. Finally, we get the last simulated aging images of the human face by the fusion algorithm. Experiments are carried out on the FG-NET dataset, and the experimental results show that the simulated face images have less differences from the original image and have a good face aging simulation effect.
© 2017 SPIE and IS&T
Yuancheng Li, Yuancheng Li, Yan Li, Yan Li, } "Face aging effect simulation model based on multilayer representation and shearlet transform," Journal of Electronic Imaging 26(5), 053011 (19 September 2017). https://doi.org/10.1117/1.JEI.26.5.053011 . Submission: Received: 11 April 2017; Accepted: 23 August 2017
Received: 11 April 2017; Accepted: 23 August 2017; Published: 19 September 2017
JOURNAL ARTICLE
11 PAGES


SHARE
RELATED CONTENT


Back to Top