ATDetect: Face Detection and Keypoint Extraction at Range and Altitude

Chun Pong Lau, Maitreya Suin, Rama Chellappa

Abstract: Face detection and alignment are the crucial preprocessing steps in face recognition. While face detection works well in ideal situations, the performance deteriorates significantly when the image is degraded, due to factors such as blur, deformation, low resolution, and extreme headpose. However, there are very few works on face detection with realistic data captured from a long range (100m to 500m) and high altitude (30° to 50° pitch angle). We first evaluated several state-of-the-art methods on data collected at ranges of 100-500 meters and large pitch angles. One challenge is videos captured from long ranges usually lack bounding boxes and keypoint annotations, needed for training deep networks. This motivates us to develop a face detection and alignment algorithm that could perform effectively on videos captured from a long range and high altitude without groundtruth annotations. Moreover, meta information such as age, gender, and headpose of the subject could help face recognition. Therefore, we propose a single-stage face localization model ATDetect, which detects face bounding boxes, keypoints, and meta information simultaneously with realistic video captured at range and altitude.