Large-scale dataset and benchmarking for hand and face detection focused on sign language

Abstract

Object detection is an important preprocessing technique for sign language recognition, allowing focus on the most important parts of the image. This paper introduces a new large-scale dataset for hand and face detection in sign language context, mitigating the lack of data for this problem. We evaluated different object detection architectures to find the best trade-off between computational cost and mean Average Precision (mAP). The proposed dataset contains 477,480 annotated images. The most accurate detector (CenterNet) achieved an mAP of 96.7%. Furthermore, the optimizations made to the models reduced the inference time up to 74% in the best scenario.