ML Anthology

Kernel ELM and CNN Based Facial Age Estimation

Furkan Gürpinar, Heysem Kaya, Hamdi Dibeklioglu, Albert Ali Salah
CVPRW 2016 pp. 785-791
doi:10.1109/CVPRW.2016.103 /cvprw/2016/gurpinar2016cvprw-kernel/

Abstract

We propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pretrained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

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Cite

Text

Gürpinar et al. "Kernel ELM and CNN Based Facial Age Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.103

Markdown

[Gürpinar et al. "Kernel ELM and CNN Based Facial Age Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/gurpinar2016cvprw-kernel/) doi:10.1109/CVPRW.2016.103

BibTeX

@inproceedings{gurpinar2016cvprw-kernel,
  title     = {{Kernel ELM and CNN Based Facial Age Estimation}},
  author    = {Gürpinar, Furkan and Kaya, Heysem and Dibeklioglu, Hamdi and Salah, Albert Ali},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2016},
  pages     = {785-791},
  doi       = {10.1109/CVPRW.2016.103},
  url       = {https://mlanthology.org/cvprw/2016/gurpinar2016cvprw-kernel/}
}