Face age estimation using patch-based hidden Markov model supervectors

Xiaodan Zhuang, Xi Zhou, Mark Hasegawa-Johnson, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent studies in patch-based Gaussian Mixture Model (GMM) approaches for face age estimation present promising results. We propose using a hidden Markov model (HMM) supervector to represent face image patches, to improve from the previous GMM supervector approach by capturing the spatial structure of human faces and loosening the assumption of identical face patch distribution within a face image. The Euclidean distance of HMM supervectors constructed from two face images measures the similarity of the human faces, derived from the approximated Kullback-Leibler divergence between the joint distributions of patches with implicit unsupervised alignment of different regions in two human faces. The proposed HMM supervector approach compares favorably with the GMM supervector approach in face age estimation on a large face dataset.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
StatePublished - Dec 1 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: Dec 8 2008Dec 11 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2008 19th International Conference on Pattern Recognition, ICPR 2008
CountryUnited States
CityTampa, FL
Period12/8/0812/11/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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