Emergent Graphs with PCA-features for Improved Face Recognition
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Built on the principles of “Learning from Nature” and “Self‐organization” Elastic Bunch Graph Matching for face recognition is a defining example for Organic Computing methodology. Here, we follow these principles further to advance the method in two respects. First, the requirement for manual annotation of landmarks is reduced to one single face, from which a self‐organizing selection process gradually builds up the bunches by adding the most similar face to the bunch graph and then recalculating the matching. Second, the resulting bunches are replaced by the principal components of the nodes of all persons in the database. The similarity function is restricted to a suitable subset of these components. The additional self‐organizing processes lead to improved precision of landmark localization and recognition rates. Altogether, an improved data structure for face storage has emerged from the simple presentation of examples in a minimally supervised way.Keywords:
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Abstract Two experiments investigated how people develop different landmark knowledge at decision points. Participants learned a route in a virtual city once or five times. One distinctive landmark was placed at each intersection of the route. At test, participants were released at each intersection according to the learning order and were required to determine the turning direction. At each intersection, the landmark was removed (no landmark), correctly placed (one landmark), duplicated on the other side (two identical landmarks), or misplaced from another intersection (two different landmarks) to disrupt the landmark sequence. The results suggested that humans develop different landmark knowledge (landmark knowledge for guidance, landmark knowledge for place recognition and knowledge of landmark sequence) with different navigation experience.
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