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Deep Learning Features for Face Age Estimation: Better Than Human?

  • Krzysztof KotowskiEmail author
  • Katarzyna Stapor
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)

Abstract

Deep convolutional neural networks have the ability to infer highly representative features from data. We decided to use this power for the purpose of estimating the human face age from a single colour image. We trained the Support Vector Machine regression model on raw feature vectors from the FaceNet deep neural network pretrained for face recognition. Our proposed method is a simple but effective FaceNet extension which does not need large scale data. In order to measure the accuracy of our approach, we proposed a test procedure on the FACES database for which we achieved the mean absolute error of 5.18 and the mean error of 0.09 years. Then, we conducted an experiment employing 78 students and showed that our method outperforms human for faces in the regular upright orientation. For vertically inverted faces, we reported an age underestimation trend in responses of students and our method.

Keywords

Deep neural networks Age estimation Face analysis 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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