Robust facial feature extraction using embedded Hidden Markov Model for face recognition under large pose variation

Ping Han Lee, Yun Wen Wang, Jison Hsu, Ming Hsuan Yang, Yi Ping Hung

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

5 Citations (Scopus)

Abstract

We propose an algorithm for extracting facial features robustly from images for face recognition under large pose variation. Rectangular facial features are retrieved via the by-products of an embedded Hidden Markov Model (HMM) which decodes an observed face image into a state sequence. While an HMM is able to segment images into features at a fixed pose, multiple HMMs are trained for each individual to robustly extract features under large pose variation. Using the extracted features of each individual, appearance models based on subspaces are constructed for face identification and verification. The effectiveness of the proposed approach is validated through empirical studies against numerous methods using the CMU PIE database. Our experiments demonstrate that the proposed approach is able to extract facial features robustly, thereby rendering superior results in identification and superior performance in verification under large pose variation.

Original languageEnglish
Title of host publicationProceedings of IAPR Conference on Machine Vision Applications, MVA 2007
Pages392-395
Number of pages4
Publication statusPublished - 2007 Dec 1
Event10th IAPR Conference on Machine Vision Applications, MVA 2007 - Tokyo, Japan
Duration: 2007 May 162007 May 18

Publication series

NameProceedings of IAPR Conference on Machine Vision Applications, MVA 2007

Other

Other10th IAPR Conference on Machine Vision Applications, MVA 2007
CountryJapan
CityTokyo
Period07/5/1607/5/18

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Lee, P. H., Wang, Y. W., Hsu, J., Yang, M. H., & Hung, Y. P. (2007). Robust facial feature extraction using embedded Hidden Markov Model for face recognition under large pose variation. In Proceedings of IAPR Conference on Machine Vision Applications, MVA 2007 (pp. 392-395). (Proceedings of IAPR Conference on Machine Vision Applications, MVA 2007).