Computer Vision News Computer Vision News 16 soft-biometrics, like gender or ethnicity, require consideration of many different facial features spatially distant from one another. One of the most challenging aspects of Peter’s research involves manipulating these global softbiometrics without erasing all the biometric utility. “When trying to preserve the privacy of global soft-biometric attributes, you need to modify many regions on the face,” he explains. “The same goes if you’re trying to do privacy enhancement on the template level. If you say, let’s remove gender information from a face, and you’re trying to modify all those cues, you can end up with an image which doesn’t have any identity-related information at all.” Peter approached solving this problem from two different angles. He explored the first in PrivacyProber, the first contribution of his PhD dissertation, which involved working on an image level, manipulating specific regions to protect soft biometrics while retaining identity. Although many state-of-the-art soft-biometric privacy-enhancing techniques employ Soft-biometric attributes can be divided based on the proximity of pixels that encode them: local soft-biometrics have pixels in close proximity, while cues, from which global soft-biometrics can be inferred, are extracted by a combination of local ones. Therefore, to enhance privacy of a global soft-biometric attribute, multiple regions on an image should be manipulated, however such manipuations decrease the amount of discriminative, identity-related. In our work, we focused on exploring the trade-offs between privacy and utility. European Biometrics Research Award
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