Bayesian methods for face recognition from video

Rama Chellappa, Shaohua Zhou, Baoxin Li

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

5 Scopus citations

Abstract

Face recognition (FR) from video necessitates simultaneously solving two tasks, recognition and tracking. To accommodate the video, a time series state space model is introduced in a Bayesian approach. Given this model, the goal reduces to estimating the posterior distribution of the state vector given the observations up to the present. The Sequential Importance Sampling (SIS) technique is invoked to generate a numerical solution to this model. However, the ultimate goal is to estimate the posterior distribution of the identify of humans for recognition purposes. Presented here are two methods to approximate the above distribution under different experimental scenarios.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - 2002
Externally publishedYes
Event2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

Other

Other2002 IEEE International Conference on Acoustic, Speech, and Signal Processing
CountryUnited States
CityOrlando, FL
Period5/13/025/17/02

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

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    Chellappa, R., Zhou, S., & Li, B. (2002). Bayesian methods for face recognition from video. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 4)