Facial age estimation by multilinear subspace analysis

Xin Geng, Kate Smith-Miles

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

32 Citations (Scopus)

Abstract

Automatic estimation of human facial age is an interesting yet challenging topic appearing in recent years. Since different people might age in different ways, solving the problem of age estimation involves two semantic labels: identity and age. In this paper, aging face images are organized in a third-order tensor according to both identity and age. Due to the difficulty in data collection, the aging pattern for each person in the training set is always incomplete. Therefore, the tensor contains a large amount of missing values. Through a series of multilinear subspace analysis algorithms operating on tensor with missing values, the aging pattern contained in the training aging images can be iteratively learned and be used to predict the age of a given test image. In the experiment, the proposed method not only outperforms the existing algorithms, but also exceeds the human ability in age estimation.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages865-868
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2009 - Taipei Taiwan, Taipei, Taiwan
Duration: 19 Apr 200924 Apr 2009

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2009
CountryTaiwan
CityTaipei
Period19/04/0924/04/09

Keywords

  • Facial age estimation
  • Machine vision
  • Pattern recognition
  • Tensor analysis

Cite this

Geng, X., & Smith-Miles, K. (2009). Facial age estimation by multilinear subspace analysis. In 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009 (pp. 865-868). [4959721] https://doi.org/10.1109/ICASSP.2009.4959721