Real-time face swapping as a tool for understanding infant self-recognition

TL;DR

This paper introduces a real-time, non-intrusive face-swapping system based on 3D visual tracking, designed to aid in infant self-recognition studies and adaptable for children with Autism Spectrum Disorder.

Contribution

It presents a novel real-time face-swapping method that is accurate, non-constraining, and suitable for sensitive populations like children with ASD.

Findings

Achieves real-time performance through parallel computing.

Effective for experiments involving infants and children with ASD.

Provides a non-constraint face-swapper system for psychological studies.

Abstract

To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods.