Face Detection in the Darkness Using Infrared Imaging: A Deep Learning-Based Study

Face detection is one of the most important research topics in the field of computer vision, and it is also the premise and an essential part of face recognition. With the advent of deep learning-based techniques, the performance of face detection has been largely improved and more and more daily applications have been witnessed. However, face detection is greatly affected by environmental illumination. Most of existing face detection algorithms neglect harsh illumination conditions such as nighttime condition where lighting is insufficient or it is totally dark. These conditions are often encountered in real-world scenarios, e.g., nighttime surveillance in law enforcement or civil settings. How to overcome the problem of face detection in the darkness becomes a critical and urgent demand. We thus in this paper study face detection in the darkness using infrared (IR) imaging. We build an IR face detection dataset and utilize a deep learning-based model to study the face detection performance. Specifically, the deep learning model is a Single Stage Detector which has the advantage of fast speed and lower computation cost compared with other face detectors that consists of multiple stages. In the experiment, we also compare the performance of the deep learning model with that of a well-known traditional face detection algorithm, Harr (or Adaboost). In terms of precision, our model significantly outperforms Harr by more than 30% | a dramatic boost from 68.75% to 98.01%, which suggests our deep learning-based method with IR imaging can indeed meet the requirement of real-world nighttime face detection applications.