Computer Vision News Computer Vision News 18 Although these methods are effective, they are not without limitations, which leaves plenty of room for extensions to this work. One key challenge is the correlation between certain soft-biometric attributes. “A beard reveals information about both age and gender, for example, so you’re limited in how much you can target each of those without affecting the other,” Peter points out. “Another interesting result is that different recognition models encode softbiometric information in different ways with different degrees of correlation.” Ultimately, training face recognition models to inherently protect soft biometrics could be a more robust long-term solution. By embedding these features into the design of recognition networks from the outset, it may be possible to achieve better privacy protection while maintaining the high accuracy biometric systems require. Our next contribution was "ASPECD: Adaptable Soft-biometric Privacy-Enhancement using Centroid Decoding". In this work, we do not focus on raw face images, but on face templates (i.e. vectors extracted from face, from which similarity between faces can be calculated). It supports that users can have different privacy-related preferences (see arrows of different colors, where red user would only want to preserve privacy of ethnicity, black for gender and ethnicity, etc.), but the comparison between templates, protected with different preferences is still possible. European Biometrics Research Award
RkJQdWJsaXNoZXIy NTc3NzU=