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 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics
Cite this
Bayesian methods for face recognition from video. / Chellappa, Rama; Zhou, Shaohua; Li, Baoxin.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 4 2002.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
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
VL - 4
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -