The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds.

Homomorphic Encryption (HE) has become a well-known tool for privacy-preserving recognition in biometric systems. Despite some important advantages of HE (such as preservation of recognition accuracy), there are two main drawbacks in the application of HE to biometric recognition systems: first, the security of the system solely depends on the secrecy of the private (decryption) key; second, the computational costs of the operations on the ciphertexts are expensive. To address these challenges, in this paper we propose a hybrid scheme for the protection of biometric templates, which combines cancelable biometrics (CB) methods and HE. Applying CB prior to HE enhances both the security and privacy of the overall system, since the protected templates remain irreversible even if the secret keys are leaked (commonly referred to as the full disclosure scenario). In addition, we can reduce the dimensionality of templates using CB before applying HE, which speeds up the computation over the ciphertexts. We use BioHashing, Multi-Layer Perceptron (MLP) hashing, and Index-of-Maximum (IoM) hashing as different CB methods, and for each of these schemes, we propose a method for computing scores between hybrid-protected templates in the encrypted domain. We evaluate our proposed hybrid scheme using different state-of-the-art face recognition models (Ar-cFace, ElasticFace, and FaceNet) on the MOBIO and LFW datasets. The source code of our experiments is publicly available, so our work can be fully reproduced.

In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i.e. cancelable, deep face templates. As such, this approach represents to the best of the authors' knowledge the first generic indexing scheme that can be applied to arbitrary cancelable face templates (o binary representation). In experiments, deep face templates are obtained from the Labelled Faces in the Wild (LFW) dataset using the ArcFace face recognition system for feature extraction. Protected templates are then generated by employing different cancelable biometric schemes, i.e. BioHashing and two variants of Index-of-Maximum Hashing. The proposed indexing scheme is evaluated on closed- and open-set identification scenarios. It is shown to maintain the recognition accuracy of the baseline system while reducing the penetration rate and hence the workload of identifications to approximately 40%.

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.