Efficient Face Detection with Audio-Based Region Proposals for Human-Robot Interactions

TL;DR

This paper introduces an audio-based attention mechanism to efficiently localize speech sources, reducing computational load in face detection for human-robot interactions without sacrificing significant accuracy.

Contribution

It proposes a novel audio-driven region proposal method that enhances face detection efficiency in robotics by lowering processing requirements.

Findings

Reduces computational load significantly

Maintains acceptable face detection accuracy

Flexible approach adaptable to various applications

Abstract

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.