PhD Colloquium: Diversity-driven Hopfield Neural Network Ensembles for Face Detection

5 June 2019

Abstract. In this thesis, we present a hybrid ensemble classifier approach for face detection and head-pose estimation.

A key factor of ensemble classifiers is diversity. Therefore, we aim to use simple features and classifiers and focus on increasing diversity. We use the pixel sum of several rectangles, arranged in different geometrical structures as features and two classifiers which uses template matching and an additional Hopfield Neural Network to compare a feature with a learned pattern. Finally, we combine features and classifiers to a hybrid ensemble architecture to further increase diversity.

Wednesday, 05 June 2019, 09:30, Room D-220, Informatikum

Speaker: Nils Meins, PhD Candidate at group WTM