Bayesian methods for face recognition from video

Face recognition (FR) from video necessitates simultaneously solving two asks, 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 identity of humans for recognition purposes. Presented here are two methods to approximate the above distribution under different experimental scenarios.

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