Identity and expression recognition on low dimensional manifolds

A solution for identity and facial expression recognition is proposed using a two stage classifier approach using low dimensional representation of the geometry of the face. Face geometry is extracted from input images using Active Appearance Models (AAM) and low dimensional manifolds were then derived using Laplacian Eigen-Maps (LE) resulting in two types of manifolds, one for model identity and the other for person-specific facial expression. The first stage uses a multiclass Support Vector Machines (SVM) to establish identity across expression changes. The second stage deals with person-specific expression recognition, and is composed by a network of seven Hidden Markov Models (HMM) displaced in parallel, each one specialized on the several facial emotions analysed. The decision was made by the sequence that yielded the highest probability. For evaluation proposes a database was build consisting on 6770 images captured from 4 people exhibiting 7 different emotions. The identity overall recognition rate was 96.8%. Facial expression results are identity dependent, and the most expressive individual achieves 81.2% of overall recognition rate.