Real-time Recognition of Facial Expression using Active Appearance Model with Second Order Minimization and Neural Network

This paper proposes a technique for real-time recognition of facial expression which uses the active appearance model (AAM) with a second order minimization and a neural network. The second order minimization gives the ability of correct convergence with a little loss of frame rate to AAM. And the correctly extracted facial shape with AAM prevents the recognition of facial expression from undergoing a large error. In addition, high dimensional feature vectors of six facial expressions which consist of facial shape and texture can be dealt by the multi-layer perceptron, a type of the neural network, with a very high recognition rate of over 99%.