Abstract
Deep learning has a hierarchical network architecture to represent the complicated feature of input patterns. We have developed the adaptive structure learning method of deep belief network (adaptive DBN) that can discover an optimal number of hidden neurons for given input data in a restricted Boltzmann machine (RBM) by neuron generation–annihilation algorithm and can obtain appropriate number of hidden layers in DBN. In this paper, our model is applied to Families in Wild Multimedia (FIW): A multi-modal database for recognizing kinship. The kinship verification is a problem whether two facial images have the blood relatives or not. In this paper, the two facial images are composed into one image to recognize kinship. The classification accuracy for the developed system became higher than the traditional method.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant Number 19K12142, 19K24365 and obtained from the commissioned research by National Institute of Information and Communications Technology (NICT, 21405), Japan.
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Ichimura, T., Kamada, S. (2021). Adaptive Structural Deep Learning to Recognize Kinship Using Families in Wild Multimedia. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_46
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DOI: https://doi.org/10.1007/978-981-16-2765-1_46
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