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Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Sébastien Marcel, Hatef Otroshi Shahreza

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose MultiIVE, a new method based on IVE to secure multiple softbiometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric attributes contained in face embeddings. In particular, we consider a domain transformation using Principle Component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by stateof-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultaneously secure multiple soft-biometric attributes and support the application of embedding domain transformations before addressing the enhancement of soft-biometric privacy.

IEEE COMPUTER SOC2023

Feature-based no-reference video quality assessment using Extra Trees

Arash Amini, Hatef Otroshi Shahreza

With the emergence of social networks and improvements in the internet speed, the video data has become an ever-increasing portion of the global internet traffic. Besides the content, the quality of a video sequence is an important issue at the user end which is often affected by various factors such as compression. Therefore, monitoring the quality is crucial for the video content and service providers. A simple monitoring approach is to compare the raw video content (uncompressed) with the received data at the receiver. In most practical scenarios, however, the reference video sequence is not available. Consequently, it is desirable to have a general reference-less method for assessing the perceived quality of any given video sequence. In this paper, a no-reference video quality assessment technique based on video features is proposed. In particular, a long list of video features (21 sets of features, each consisting of 1 to 216 features) is considered and all possible combinations (2(21) - 1) for training an Extra Trees regressor is examined. This choice of the regressor is wisely selected and is observed to perform better than other common regressors. The results reveal that the top 20 performing feature subsets all outperformthe existing featurebased assessment methods in terms of the Pearson linear correlation coefficient (PLCC) or the Spearman rank order correlation coefficient (SROCC). Specially, the best performing regressor achieves PLCC = 0.786 on the test data over the KonVid-1k dataset. It is believed that the results of the comprehensive comparison could be potentially useful for other feature-based video-related problems. The source codes of the implementations are publicly available.

WILEY2022

Hybrid Protection of Biometric Templates by Combining Homomorphic Encryption and Cancelable Biometrics

Sébastien Marcel, Hatef Otroshi Shahreza

Homomorphic Encryption (HE) has become a wellknown 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-ofMaximum (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 (ArcFace, 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.

IEEE2022