Bayesian methods for face recognition from video

Rama Chellappa; Shaohua Zhou; Baoxin Li

Bayesian methods for face recognition from video

Rama Chellappa, Shaohua Zhou, Baoxin Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
State	Published - 2002
Externally published	Yes
Event	2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States Duration: May 13 2002 → May 17 2002

Other

Other	2002 IEEE International Conference on Acoustic, Speech, and Signal Processing
Country	United States
City	Orlando, FL
Period	5/13/02 → 5/17/02

ASJC Scopus subject areas

Electrical and Electronic Engineering
Signal Processing
Acoustics and Ultrasonics

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title = "Bayesian methods for face recognition from video",

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.",

author = "Rama Chellappa and Shaohua Zhou and Baoxin Li",

year = "2002",

language = "English (US)",

volume = "4",

booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

note = "2002 IEEE International Conference on Acoustic, Speech, and Signal Processing ; Conference date: 13-05-2002 Through 17-05-2002",

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T1 - Bayesian methods for face recognition from video

AU - Chellappa, Rama

AU - Zhou, Shaohua

AU - Li, Baoxin

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0036293477&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036293477&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0036293477

VL - 4

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2002 IEEE International Conference on Acoustic, Speech, and Signal Processing

Y2 - 13 May 2002 through 17 May 2002

ER -

Bayesian methods for face recognition from video

Abstract

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ASJC Scopus subject areas

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