| 1. |
- Rosberg, Felix, et al.
(författare)
-
Comparing Facial Expressions for Face Swapping Evaluation with Supervised Contrastive Representation Learning
- 2021
-
Ingår i: 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). - Piscataway : IEEE. - 9781665431767
-
Konferensbidrag (refereegranskat)abstract
- Measuring and comparing facial expression have several practical applications. One such application is to measure the facial expression embedding, and to compare distances between those expressions embeddings in order to determine the identity- and face swapping algorithms' capabilities in preserving the facial expression information. One useful aspect is to present how well the expressions are preserved while anonymizing facial data during privacy aware data collection. We show that a weighted supervised contrastive learning is a strong approach for learning facial expression representation embeddings and dealing with the class imbalance bias. By feeding a classifier-head with the learned embeddings we reach competitive state-of-the-art results. Furthermore, we demonstrate the use case of measuring the distance between the expressions of a target face, a source face and the anonymized target face in the facial anonymization context. © 2021 IEEE.
|
|
| 2. |
- Rosberg, Felix, 1995-, et al.
(författare)
-
Towards Privacy Aware Data collection in Traffic : A Proposed Method for Measuring Facial Anonymity
- 2021
-
Ingår i: Fast-Zero 2021 Proceedings. - Chiyoda : JSAE.
-
Konferensbidrag (refereegranskat)abstract
- Developing a machine learning-based vehicular safety system that is effective and generalizes well, capable of coping with all the different scenarios in real traffic is a challenge that requires large amounts of data. Especially visual data for when you want an autonomous vehicle to make decisions based on peoples’ possible intent revealed by the facial expression and eye gaze of nearby pedestrians. The problem with collecting this kind of data is the privacy issues and conflict with current laws like General Data Protection Regulation (GDPR). To deal with this problem we can anonymise faces with current identity and face swapping techniques. To evaluate the performance and interpretation of the anonymization process, there is a need for a metric to measure how well these faces are anonymized that takes identity leakage into consideration. To our knowledge, there is currently no such investigation for this problem. However, our method is based on current facial recognition methods and how recent face swapping work determines identity transfer performance. Our suggestion is to utilize state-of-the-art identity encoders like FaceNet and ArcFace to make use of the embedding vectors to measure anonymity. We provide qualitative results that show the applicability of publicly available identity encoders for measuring anonymity. We further strengthen the applicability of how these encoders behave on the VGGFace2 dataset compared to samples that have had their identity changed by Faceshifter, along with a survey regarding the anonymization procedure to pinpoint how strong facial anonymization is compared the vector distance measurements.
|
|
| 3. |
- Torstensson, Martin, et al.
(författare)
-
Data Leakage In Anonymization Methods : Towards explainable machine learning
- 2021
-
Konferensbidrag (refereegranskat)abstract
- Anonymization methods are one potential way of alleviating the risks of capturing personal information during data collections. The work presented here is based on one such method that, in turn, is based on generating images through machine learning to replace the original images. The chosen method merges both the original image and the generated one resulting in a risk of information from the original image leaking through to the final result. Here a possible approach to measure how much influence the original image has on the final product is presented .
|
|
