Learning Emotional-Blinded Face Representations
This addresses privacy and fairness concerns in AI systems by enabling compliance with data protection regulations, though it is incremental as it builds on existing face representation techniques.
The paper tackled the problem of protecting sensitive emotional information in facial data by proposing two methods to learn face representations that are blind to emotional expressions, showing that emotion recognition information can be eliminated while only slightly affecting performance in tasks like subject verification, gender recognition, and ethnicity classification.
We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which enforce data controllers to protect any kind of sensitive information involved in automatic processes. The advances in Affective Computing have contributed to improve human-machine interfaces but, at the same time, the capacity to monitorize emotional responses triggers potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these expression-blinded facial features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers in a case study of attractiveness classification with respect to a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.