Beyond Wisdom of Crowds: Deep Uncertainty Coding for Apparent Age Estimation

Yanlin Qian, Ke Chen, Dan Yang, Joni Kämäräinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Conventional algorithms for apparent age estimation make a strong assumption that the wisdom of crowds (i.e. the mean value of inconsistently annotated age labels) is superior to any individual. In this paper, we go beyond such an assumption and instead of averaging to remove label uncertainty we cope with label uncertainty in training by introducing a new concept of uncertainty coding. Uncertainty coding encodes multiple unary apparent age labels into a binary vector indicating age range (uncertainty) in the target space. We traverse through convolutional features and architecture to learn deep uncertainty mapping, whose predicted codes as mid-level features are fed into the second layer regressor that outputs a target prediction. We conduct experiments on the ChaLearn and FG-NET benchmarks to demonstrate superior performance of the proposed deep uncertainty coding to the state-of-the-arts.
Original languageEnglish
Title of host publication2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
Number of pages5
ISBN (Electronic)978-1-7281-2325-7
ISBN (Print)978-1-7281-2326-4
Publication statusPublished - 2019
Publication typeA4 Article in conference proceedings
EventInternational Conference on Image, Vision and Computing -
Duration: 1 Jan 2000 → …


ConferenceInternational Conference on Image, Vision and Computing
Period1/01/00 → …

Publication forum classification

  • Publication forum level 1


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