| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
Higher-Order Spectral features of signals provide nonlinear signal features unavailable in power spectra. However, their high-dimensionality, sparseness and high computational cost were obstacles for general use for signal classification. In this work, we attempted to solve this issue by joint use of Local Higher-Order Moment Spectrum (LHOMS) kernel functions and gradient based learning of neural networks. A modified image feature extraction method by LHOMS kernel was applied to iris authentication. It was found that the method allows authentication robust to additive noise to the iris image. Also, a novel method for inheriting the probability density of parameters in transfer learning of convolutional neural networks was introduced. The novel transfer learning improved the training efficiency when compared with the existing methods.
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