Stochastic Fuzzy Modeling for Ear Imaging Based Child Identification
针对第三世界国家儿童疫苗递送中的身份识别难题，提出用随机模糊模型匹配非标准条件下拍摄的耳朵照片，通过力场变换提取局部特征并建立随机模糊系统，实现鲁棒匹配。
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.