Abstract
AbstractComparing ear photographs is considered to be an important aspect of disaster victim identification and other forensic and security applications. An interesting approach concerns the construction of 3D ear models by fitting the parameters of a ‘standard’ ear shape, in order to transform it into an optimal approximation of a 3D ear image. A feature list is then extracted from each 3D ear model and used in the recognition process. In this paper, we study how the quality and usability of a recognition process can be improved by computational intelligence techniques. More specifically, we study and illustrate how bipolar data modelling and aggregation techniques can be used for improving the representation and handling of data imperfections. A novel bipolar measure for computing the similarity between corresponding feature lists is proposed. This measure is based on the Minkowski distance, but explicitly deals with hesitation that is caused by bad image quality. Moreover, we investigate how forensic e...
Highlights
The purpose of this paper is proposing a theoretical framework for novel techniques, which make ear comparison more human centric
The paper first presents a framework for assessing data quality and reflecting the hesitation caused by bad data quality in the comparison results
This provides decision makers with useful extra information. Another novelty in the paper is the integration of an advanced, configurable aggregation structure, supporting the incorporation of forensic expert knowledge in the comparison process
Summary
Consider the situation where a set of ear photos of a victim has to be compared with a set of ear photos of a missing person This is one of the tasks forensic experts might have to deal with when trying to identify a victim’s body. We investigate ear recognition techniques which allow to explicitly reflect forensic expert knowledge on ear identification, and provide semantically richer information on the quality of retrieved results. Results of elementary comparisons results are combined using a hierarchic logic scoring of preferences (LSP)[8] aggregation structure This provides us with facilities to adequately model and incorporate (forensic) expert knowledge on ear comparison issues. It describes how corresponding (groups of) feature points from two ear models can be compared, using a novel bipolar similarity measure.
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