Feature Fusion Hierarchies for gender classification

We present a hierarchical feature fusion model for image classification that is constructed by an evolutionary learning algorithm. The model has the ability to combine local patches whose location, width and height are automatically determined during learning. The representational framework takes the form of a two-level hierarchy which combines feature fusion and decision fusion into a unified model. The structure of the hierarchy itself is constructed automatically during learning to produce optimal local feature combinations. A comparative evaluation of different classifiers is provided on a challenging gender classification image database. It demonstrates the effectiveness of these Feature Fusion Hierarchies (FFH).

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Fabien Scalzo, George Bebis, Mircea Nicolescu, Leandro Loss, Alireza Tavakkoli
• Keywords: Computer Vision, Feature Fusion Hierarchies, Feature Fusion Model, ICPR 2008, Optimal Local Feature