We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
SFPD: Simultaneous Face and Person Detection in Real-Time for Human–Robot Interaction.
- Authors
Fiedler, Marc-André; Werner, Philipp; Khalifa, Aly; Al-Hamadi, Ayoub
- Abstract
Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human–robot interaction.
- Subjects
HUMAN-robot interaction; HUMAN behavior; FACIAL expression; COMPUTER vision; HUMAN facial recognition software; HUMAN activity recognition
- Publication
Sensors (14248220), 2021, Vol 21, Issue 17, p5918
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21175918