ML Anthology

Component Biologically Inspired Features with Moving Segmentation for Age Estimation

Gee-Sern Jison Hsu, Yi-Tseng Cheng, Choon-Ching Ng, Moi Hoon Yap
CVPRW 2017 pp. 540-547
doi:10.1109/CVPRW.2017.81 /cvprw/2017/hsu2017cvprw-component/

Abstract

We propose the Component Bio-Inspired Feature (CBIF) with a moving segmentation scheme for age estimation. The CBIF defines a superset for the commonly used Bio-Inspired Feature (BIF) with more parameters and flexibility in settings, resulting in features with abundant characteristics. An in-depth study is performed for the determination of the parameters good for capturing age-related traits. The moving segmentation is proposed to better determine the age boundaries good for age grouping, and improve the overall performance. The proposed approach is evaluated on two common benchmarks, FG-NET and MORPH databases, and compared with contemporary approaches to demonstrate its efficacy.

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Cite

Text

Hsu et al. "Component Biologically Inspired Features with Moving Segmentation for Age Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.81

Markdown

[Hsu et al. "Component Biologically Inspired Features with Moving Segmentation for Age Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/hsu2017cvprw-component/) doi:10.1109/CVPRW.2017.81

BibTeX

@inproceedings{hsu2017cvprw-component,
  title     = {{Component Biologically Inspired Features with Moving Segmentation for Age Estimation}},
  author    = {Hsu, Gee-Sern Jison and Cheng, Yi-Tseng and Ng, Choon-Ching and Yap, Moi Hoon},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {540-547},
  doi       = {10.1109/CVPRW.2017.81},
  url       = {https://mlanthology.org/cvprw/2017/hsu2017cvprw-component/}
}