This study introduces a wearable facial recognition system for face blindness, or prosopagnosia, rehabilitation. Prosopagnosia is the inability to recognize familiar faces, which affects 2.5% of the world population (148 million people). The design and implementation of a facial recognition system tailored to patients with prosopagnosia is a priority in the field of clinical neuroscience. The goal of this study is to demonstrate the feasibility of implementing a wearable stand-alone (not connected to a PC or a smartphone) system-on-chip (SoC) that performs facial recognition and could be used to assist individuals affected by prosopagnosia.
This system is designed as an autonomous embedded platform built on eyewear with SoC and a custom designed circuit board. The implementation is based on the open source computer vision image processing algorithms embedded within a compact-scale processor. The advantages of the device are its lightness, compactness, single independent image processing capability and long operational time. The system performs real-time facial recognition and informs the user of the results by displaying the name of the recognized person.