  Efficient face detection is critical to provide natural human-robot
interactions. However, computer vision tends to involve a large computational
load due to the amount of data (i.e. pixels) that needs to be processed in a
short amount of time. This is undesirable on robotics platforms where multiple
processes need to run in parallel and where the processing power is limited by
portability constraints. Existing solutions often involve reducing image
quality which can negatively impact processing. The literature also reports
methods to generate regions of interest in images from pixel data. Although it
is a promising idea, these methods often involve heavy vision algorithms. In
this paper, we evaluate how audio can be used to generate regions of interest
in optical images to reduce the number of pixels to process with computer
vision. Thereby, we propose a unique attention mechanism to localize a speech
source and evaluate its impact on an existing face detection algorithm. Our
results show that the attention mechanism reduces the computational load and
offers an interesting trade-off between speed and accuracy. The proposed
pipeline is flexible and can be easily adapted to other applications such as
robot surveillance, video conferences or smart glasses.
