Face detection and recognition in an unconstrained environment for mobile visual assistive system

Abstract

We present a visual assistive system that features mobile face detection and recognition in an unconstrained environment from a mobile source using convolutional neural networks. The goal of the system is to effectively detect individuals that approach facing towards the person equipped with the system. We find that face detection and recognition becomes a very difficult task due to the movement of the user which causes camera shakes resulting in motion blur and noise in the input for the visual assistive system. Due to the shortage of related datasets, we create a dataset of videos captured from a mobile source that features motion blur and noise from camera shakes. This makes the application a very challenging aspect of face detection and recognition in unconstrained environments. The performance of the convolutional neural network is further compared with a cascade classifier. The results show promising performance in daylight and artificial lighting conditions while the challenges lie for moonlight conditions with the need for reduction of false positives in order to develop a robust system. We also provide a framework for implementation of the system with smartphones and wearable devices for video input and auditory notification from the system to guide the visually impaired.