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

Abstract

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.

Technical Trends and Competitive Situation in Respect of Metahuman—From Product Modules and Technical Topics to Patent Data

“…The authors found some prominent technologies that gained significant importance over this period. We also compared our findings with other studies, showing that facial recognition technology, capture-based and synthesis motion, rendering technology, and "Digital Human Modeling" based on virtual reality are all topics of interest to scholars [71][72][73][74]. However, the "Intelligence-Driven" module is hardly mentioned in previous research, possibly because there has been limited exploration of modules in the practical dimension.…”

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

“…availability attack in federated learning). Rapidly however, this attack channel became prominently featured as an integrity vulnerability in the backdoor attack literature [31], [70], [79]- [89]. In this context, attacks are targeted (i.e.…”

Implementasi Sistem Kasir Digital Berbasis Teknologi Deteksi Tangan Computer Vision dan OpenCV

“…Penelitian ini memiliki manfaat yang signifikan, terutama dalam meningkatkan efisiensi serta kepuasan pelanggan, dan menciptakan hal yang berbeda dalam pemesanan menu dan menjadi sesuatu yang menari untuk pelanggan [10]. Dengan menggunakan Teknologi kecerdasan buatan, Computer Vision dangan metode CNN, Penelitian ini akan menciptakan sesuatu pengalaman pelanggan yang lebih mudah dan cepat dalam memesan dan membayar menggunakan sistem digital dioutlet dengan pelayanan secara virtual dalam menggunakan teknologi kecerdasan buatan [11]. Hasil pengembangan ini memberikan hasil positif untuk pelayanan dan mempersingkat waktu tunggu pelanggan setelah pemesanan dan waktu pembayaran yang mudah, dapat memberikan kontribusi bagi perkembangan industri makanan dan minuman serta pengembangan teknologi dimasa depan [12].…”