Experimental study of multi-level regional voting scheme and its application in human face recognition.

Based on previous regional voting scheme model studies and assumptions, we extend studies from two-level voting scheme models to multi-level voting scheme models. Due to mathematical complexity, we use the Monte Carlo approach in studying the stability of the multi-level regional voting scheme with respect to region sizes and levels. Using this model, we are able to obtain the stability characteristics of the national voting scheme, the two-level regional voting scheme, and the multi-level regional voting scheme, and apply it in FERET human face database. According to our study, we verify again that the regional voting scheme (including the two-level regional voting scheme and multi-level regional voting scheme) is always more stable than the national voting scheme. We find that the stability of the multi-level regional voting scheme is not as good as the stability of the two-level regional voting scheme when region size is within a certain range. Out of this range, the multi-level regional voting scheme may compete with the two-level voting scheme. We conclude that the multi-level regional voting scheme may be comparable to the two-level regional voting scheme and prove our conclusion in the face recognition application.