Adaptive Structural Deep Learning to Recognize Kinship Using Families in Wild Multimedia

Autor:	Shin Kamada, Takumi Ichimura
Rok vydání:	2021
Předmět:	Restricted Boltzmann machine Network architecture Multimedia business.industry Computer science Deep learning computer.software_genre Facial recognition system Image (mathematics) Deep belief network Feature (machine learning) Kinship Artificial intelligence business computer
Zdroj:	Intelligent Decision Technologies ISBN: 9789811627644 KES-IDT
DOI:	10.1007/978-981-16-2765-1_46
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::53b8a771c823510a6d29a4a8b7a9820f https://doi.org/10.1007/978-981-16-2765-1_46 Zobrazit plný text záznamu