Maximum likelihood training of the embedded HMM for face detection and recognition

Abstract

The embedded hidden Markov model (HMM) is a statistical model that can be used in many pattern recognition and computer vision applications. This model inherits the partial size invariance of the standard HMM, and, due to its pseudo two-dimensional structure, is able to model two-dimensional data such as images, better than the standard HMM. We describe the maximum likelihood training for the continuous mixture embedded HMM and present the performance of this model for face detection and recognition. The experimental results are compared with other approaches to face detection and recognition.