Active appearance model-based facial composite generation with interactive nature-inspired heuristics

The aim of this study is to automatically generate facial composites in order to match a target face, by using the active appearance model (AAM). The AAM generates a statistical model of the human face from a training set. The model parameters control both the shape and the texture of the face. We propose a system in which a human user interactively tries to optimize the AAM parameters such that the parameters generate the target face. In this study, the optimization problem is handled through using nature-inspired approaches. Experiments with interactive versions of different nature-inspired heuristics are performed. In the interactive versions of these heuristics, users participate in the experiments either by quantifying the solution quality or by selecting the most similar faces. The results of the initial experiments are promising which promote further study.