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This paper proposes a new method for extracting the sensibility (kansei) of human experts, and applies it to the design of a pearl color evaluation system. A factors identification method using sensitivity of layered neural networks finds the influential factors as the input units which have the greatest influence on all outputs. This method has been applied to the network mapping between spectral reflectance of pearls and the pearl color categories evaluated by experts, and allowed some specific wavelength bands to be identified as the essential factors contributing to the evaluation. Based on this result, a pearl color evaluation system has been designed using multi-bands images (MBI) acquired by combining a monochrome CCD camera with several interference filters corresponding to the identified major wavelength bands factors. Moreover, the identification method has been applied to find the influential factors of each image of MBI and finally the experimental results of discrimination using those factors show that this system is able to give evaluations of pearl color successfully as an expert does.

Factors Identification Using Sensitivity of Layered Neural Networks and Its Application to Pearl Color Evaluation