Kernel ELM and CNN Based Facial Age Estimation

Conference paper (2016)

Authors

F Gurpinar Boğaziçi University

H Kaya Namik Kemal University

H. Dibeklioglu Pattern Recognition and Bioinformatics -

Albert Ali Salah Boğaziçi University

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

More Info

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To reference this document use:

http://resolver.tudelft.nl/uuid:a262f023-70e2-4521-9a76-88894fad5b1e

Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

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.