Chapter

Computer Vision – ECCV 2008

Volume 5304 of the series Lecture Notes in Computer Science pp 168-181

Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition

  • Cassio P. de CamposAffiliated withElectrical, Computer and Systems Eng. Dept. Rensselaer Polytechnic Institute
  • , Yan TongAffiliated withVisualization and Computer Vision Lab, GE Global Research Center
  • , Qiang JiAffiliated withElectrical, Computer and Systems Eng. Dept. Rensselaer Polytechnic Institute

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quality of network parameters. Learning reliable parameters of Bayesian networks often requires a large amount of training data, which may be hard to acquire and may contain missing values. On the other hand, qualitative knowledge is available in many computer vision applications, and incorporating such knowledge can improve the accuracy of parameter learning. This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation. For complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectation-maximization method is proposed. This framework is applied to real image data from a facial action unit recognition problem and produces results that are similar to those of state-of-the-art methods.