Stochastic Fuzzy Modeling for Ear Imaging Based Child Identification

Abstract

The unique identification of children is crucial for information technology supported vaccine delivery to the unprivileged population of third world countries. New robust image matching algorithms are required to match two ear photographs taken under nonstandard real-world conditions such as the presence of unwanted background objects in the photographs. This paper applies stochastic fuzzy models to the robust matching of ear images. The local features of the image regions are extracted using a “force-field-like” transformation. The extracted features of an image region are modeled by a stochastic fuzzy system. A region of an image is matched to a region of another image by matching the features of an image’s region with the model of another image’s region. As the model is fuzzy as well as stochastic, a robust matching of features’ data to a model is facilitated by handling any uncertainties arising from fuzziness and randomness of the image features. The study introduces an information-theoretic index for measuring the degree of matching between image features and a model of the features. Several experiments are performed on a database of 750 ear-photographs of children (0–6 years) to justify the novel stochastic fuzzy image matching method.