Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

Biometric Systems De-Identification: Current Advancements and Future Directions

In this paper, two face de-identification methods are proposed regarding face identification hindering against a deep neural network. Our work focuses on achieving a delicate balance, so that the facial images are miss-classified by the deep network, while the human observer can still identify the persons depicted in a scene. The proposed methods are based on achieving face de-identification by partly degrading image quality in order to hinder face recognition from deep neural networks, while maintaining the highest possible image quality, at the same time. To this end, we employ de-identification methods based on singular value decomposition and image hypersphere projections, respectively. From the conducted experiments, it can be concluded that these methods are capable of reducing correct face identification rates of the VGG-face network by over 90 %. Moreover, it is shown that these error rates preserve adequate image quality as is demonstrated through the values of the complex wavelet structural similarity index, allowing face recognition by humans contrary to most face de-identification methods.

Rosen Zhelev

Papers

Quality preserving face de-identification against deep cnns