Deep-Learning-Based Adaptive Advertising with Augmented Reality

In this work we describe a system composed of deep neural networks that analyzes characteristics of customers based on their face (age, gender, and personality), as well as the ambient temperature, with the purpose of generating a personalized signal to potential buyers who pass in front of a beverage establishment; faces are automatically detected, displaying a recommendation using deep learning methods. In order to present suitable digital posters for each person, several technologies were used: Augmented reality, estimation of age, gender, and estimation of personality through the Big Five test applied to an image. The accuracy of each one of these deep neural networks is measured separately to ensure an appropriate precision over 80%. The system has been implemented into a portable solution, and is able to generate a recommendation to one or more people at the same time.

Automated summary

This is a system composed of deep neural networks that generates personalized recommendations by analyzing customers' facial characteristics and ambient temperature. The system utilizes various technologies, including augmented reality and estimation of age, gender, and personality through image-based tests. Each deep neural network achieves an accuracy of over 80%. The system is implemented as a portable solution and is capable of generating recommendations for one or multiple individuals simultaneously.