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Experimental study of different aggregation functions for modeling craniofacial correspondence in craniofacial superimposition

Craniofacial superimposition is a forensic identification method involving the overlay of a skull over the available ante-mortem photographs of a candidate missing person face and the subsequent analysis of their anatomical correspondence. Within this process, the decision making stage focuses on determining the degree of support of being the same person or not based on the analysis of some criteria assessing the skull-face morphological correspondence. That decision is usually made in a non automatic and subjective way. We aim to automate the decision making process using computer vision and soft computing methods to assist the forensic anthropologist. In a previous study we have developed several methods to measure the matching of the correspondence between the face and the skull. The accuracy of each method was calculated as its capability to discriminate in a cross-comparison identification scenario. By the use of aggregation functions we can combine the results of the different methods taking into account the corresponding individual accuracy. This allows us to provide a single global output specifying the matching of each criterion while combining the capability of different methods. In this work, we present a study of the behavior of different aggregation functions for this aim. The performance of the aggregated methods has been tested on 172 skull-face overlay problem instances of positive and negative cases. The obtained results show that Sugeno integral ranks better than the counterparts although not significant conclusions can be delivered regarding the performance.