Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications
Article dans une revue avec comité de lecture
Abstract
The face blurring of images plays a key role in protecting privacy. However, in computer vision, especially for the human pose estimation task, machine-learning models are currently trained, validated, and tested on original datasets without face blurring. Additionally, the accuracy of human pose estimation is of great importance for kinematic analysis. This analysis is relevant in areas such as occupational safety and clinical gait analysis where privacy is crucial. Therefore, in this study, we explore the impact of face blurring on human pose estimation and the subsequent kinematic analysis. Firstly, we blurred the subjects’ heads in the image dataset. Then we trained our neural networks using the face-blurred and the original unblurred dataset. Subsequently, the performances of the different models, in terms of landmark localization and joint angles, were estimated on blurred and unblurred testing data. Finally, we examined the statistical significance of the effect of face blurring on the kinematic analysis along with the strength of the effect. Our results reveal that the strength of the effect of face blurring was low and within acceptable limits (<1°). We have thus shown that for human pose estimation, face blurring guarantees subject privacy while not degrading the prediction performance of a deep learning model.
Files in this item
- Name:
- IBHGC_S_2022_JIANG.pdf
- Size:
- 2.417Mb
- Format:
- Description:
- Effect of Face Blurring on Human ...
Related items
Showing items related by title, author, creator and subject.
-
Communication avec acteEBRAHIMI, Shahin; ANGELINI, Elsa; GAJNY, Laurent; SKALLI, Wafa (IEEE, 2016)3D reconstruction of the spine using biplanar X-rays remains approximate and requires human-machine interactions to adjust the position of important features such as vertebral corners and endplates. The purpose of this ...
-
Article dans une revue avec comité de lectureGAJNY, Laurent; EBRAHIMI, Shahin; VERGARI, Claudio; ANGELINI, Elsa; SKALLI, Wafa (Springer Verlag, 2018)Purpose: To design a quasi-automated three-dimensional reconstruction method of the spine from biplanar X-rays as the daily used method in clinical routine is based on manual adjustments of a trained operator and the ...
-
Article dans une revue avec comité de lectureGAJNY, Laurent; GIRINON, François; BAYOUD, Wael; LAHKAR, Bhrigu; BONNET-LEBRUN, Aurore; ROUCH, Philippe; LAZENNEC, Jean-Yves; SKALLI, Wafa (Elsevier BV, 2022-03)Three-dimensional bone reconstructions from medical imaging are essential for biomechanical modelling and are growing tools in clinics. Several methods of lower limbs reconstruction from biplanar radiographs have ...
-
Article dans une revue avec comité de lectureLANGLAIS, Tristan; VERGARI, Claudio; ROUGEREAU, Grégoire; GAJNY, Laurent; ASSI, Ayman; GHANEM, Ismat; DUBOUSSET, Jean; VIALLE, Raphaël; PIETTON, Raphaël; SKALLI, Wafa (Elsevier BV, 2021)Objective: Our objective was to establish a corridor of normality for the external shape 3D parameters and then to assess these variables in adolescent idiopathic scoliosis (AIS). Methods: Adolescent with mild and severe ...
-
Article dans une revue avec comité de lectureVAFADAR, Saman; SKALLI, Wafa; BONNET-LEBRUN, Aurore; ASSI, Ayman; GAJNY, Laurent (Elsevier BV, 2022-05)Background. Marker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint ...