Combining information in a Bayesian network for face detection

Face detection has been an important research topic over the last 20 years. It is commonly used as a first step in face recognition and several techniques were applied in face detection, going from geometrical methods such as model matching to connectionist methods such as neural networks. This work presents a face detection system that uses a Bayesian network to combine information from different computational cheap visual operators, and is part of an ongoing project that uses a webcam to perform a reliable and accurate eye tracking. The face detection is the first step in this project. The aim in this work is to show that combining simple features in a Bayesian network helps improving the performance in a face detector system, increasing the detection rate and speeding up the face detection process.