On privacy preserving anonymization of finger-selfies

With the availability of smartphone cameras, high speed internet, and connectivity to social media, users post content on the go including check-ins, text, and images. Privacy leaks due to posts related to check-ins and text is an issue in itself, however, this paper discusses the potential leak of one's biometric information via images posted on social media. While posting photos of themselves or highlighting miniature objects, users end up posting content that leads to an irreversible loss of biometric information such as ocular region, fingerprint, knuckle print, and ear print. In this paper, we discuss the effect of the loss of the finger-selfie details from social media. We demonstrate that this could potentially lead to matching finger-selfies with livescan fingerprints. Further, to prevent the leak of the finger-selfie details, we propose privacy preserving adversarial learning algorithm. The algorithm learns a perturbation to prevent the misuse of finger-selfie towards recognition, yet keeping the visual quality intact to highlight the minuscule object. The experiments are presented on the ISPFDv1 database. Further, we propose a new publicly available Social-Media Posted Finger-selfie (SMPF) Database, containing 1, 000 finger-selfie images posted on Instagram.