Landmark-Based Facial Feature Construction and Action Unit Intensity Prediction

Human face recognition has been widely used in many fields, including biorobots, driver fatigue monitoring, and polygraph tests. However, the end-to-end models fit by most of the existing algorithms perform poorly in interpretation because complex classifiers are constructed using facial images directly. In addition, in some of the models, dynamic characteristics of subjects as individuals are not fully considered, so dynamic information is not extracted. In order to solve these problems, this paper proposes an action unit intensity prediction model. The three-dimensional coordinates of 68 landmarks of human faces are obtained based on the convolutional experts constrained local model (CE-CLM), which enables the construction of dynamic facial features. Based on the error analysis of the CE-CLM algorithm, dimension reduction of the constructed features is performed by the principal components analysis (PCA). The radial basis function (RBF) neural network is also constructed to train the action unit prediction models. The proposed method is verified by the experiments, and the overall mean square error (MSE) of the proposed method is 0.01826. Lastly, the network construction process is optimized, so that for the same training samples, the models are fitted using fewer iterations. The number of iterations is decreased by 27 on average. In summary, this paper provides a method to rapidly construct action unit (AU) intensity prediction models and constructs automatic AU intensity estimation models for facial images.