Nanni, L.; Brahnam, S.; Lumini, A.; Loreggia, A. Coupling RetinaFace and Depth Information to Filter False Positives. Appl. Sci.2023, 13, 2987.
Nanni, L.; Brahnam, S.; Lumini, A.; Loreggia, A. Coupling RetinaFace and Depth Information to Filter False Positives. Appl. Sci. 2023, 13, 2987.
Nanni, L.; Brahnam, S.; Lumini, A.; Loreggia, A. Coupling RetinaFace and Depth Information to Filter False Positives. Appl. Sci.2023, 13, 2987.
Nanni, L.; Brahnam, S.; Lumini, A.; Loreggia, A. Coupling RetinaFace and Depth Information to Filter False Positives. Appl. Sci. 2023, 13, 2987.
Abstract
Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and different lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The authors evaluate their method on a combined dataset of 549 images, containing a total of 614 upright frontal faces. The results show that the proposed method is able to significantly reduce the number of false positives without sacrificing the overall detection rate. This indicates that the use of depth information can be a useful tool for improving face detection performance.
Keywords
face detection; depth map; deep learning; filtering
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
