Kernel ELM and CNN Based Facial Age Estimation

F Gurpinar, H Kaya, Hamdi Dibeklioglu, A Ali Salah

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

43 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
EditorsLisa O'Conner
Place of PublicationPiscataway
PublisherIEEE
Pages785-791
Number of pages7
ISBN (Electronic)978-1-5090-1437-8
ISBN (Print)978-1-5090-1438-5
DOIs
Publication statusPublished - 2016
EventCVPR 2016: 29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Conference

ConferenceCVPR 2016
CountryUnited States
CityLas Vegas
Period26/06/161/07/16

Fingerprint Dive into the research topics of 'Kernel ELM and CNN Based Facial Age Estimation'. Together they form a unique fingerprint.

Cite this