Adaboost and Hopfield Neural Networks on different image representations for robust face detection

Face detection is an active research area comprising the fields of computer vision, machine learning and intelligent robotics. However, this area is still challenging due to many problems arising from image processing and the further steps necessary for the detection process. In this work we focus on Hopfield Neural Network (HNN) and ensemble learning. It extends our recent work by two components: the simultaneous usage of different image representations and combinations as well as variations in the training procedure. Using the HNN within an ensemble achieves high detection rates but shows no increase in false detection rates, as is commonly the case. We present our experimental setup and investigate the robustness of our architecture. Our results indicate, that with the presented methods the face detection system is flexible regarding varying environmental conditions, leading to a higher robustness.