Unconstrained Face Detection from a Mobile Source Using Convolutional Neural Networks

Abstract

We present unconstrained mobile face detection using convolutional neural networks which have potential application for guidance systems for visually impaired persons. We develop 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 unconstrained face detection. The performance of the convolutional neural network is 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.