Intelligent Digital Human Agent Service With Deep Learning Based-Face Recognition

This study proposes a framework for an intelligent agent information service using digital human and deep learning technology. The framework can recognize the identity of individuals using facial features and provide personalized services through a digital human. The personalized service is defined by a relevance graph based on personal data collected in advance. The proposed system can continuously evolve to recommend customized services using relevance graphs and dynamic data processing, gradually become more intelligent using additionally collected data. Moreover, it uses animation keyframe interpolation for natural and seamless digital human interaction and provides visual effects that are synchronized based on specific information collected for the intuitive service. The proposed system was tested on a school domain for two months, and a statistical domain feedback system based on a mathematical model that predicts service usage per unit time was developed using the recorded information. Additionally, we evaluate our system through user experience surveys.

Abstract licence: CC BY-NC-ND

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