A subspace method for maximum likelihood target detection

An unsupervised technique for visual target modeling which is based on density estimation in high-dimensional spaces using an eigenspace decomposition is presented and is shown to be superior to matched filtering.

Abstract

We present an unsupervised technique for visual target modeling which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. A computationally efficient and optimal estimator for a multivariate Gaussian distribution is derived. This density estimate is then used to formulate a maximum likelihood estimation framework for visual search and target detection. Our learning technique is applied to the probabilistic visual modeling and subsequent detection of facial features and is shown to be superior to matched filtering.