Dailé Osorio-Roig

Hatef Otroshi Shahreza, Pietro Melzi, Dailé Osorio-Roig et al.

In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing (i.e., IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learning based templates extracted from different physiological and behavioral biometric characteristics including face, voice, finger vein, and iris. In addition, we provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results.

Pawel Drozdowski, Fabian Stockhardt, Christian Rathgeb et al.

Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption. The proposed method is extensively evaluated in closed-set and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.

Dailé Osorio-Roig, Christian Rathgeb, Pawel Drozdowski et al.

In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes; (2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker's database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.