Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy–enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy–related research in the area of biometrics and review existing work on Biometric Privacy–Enhancing Techniques (B–PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B–PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.

Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non‐identity‐related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.

In recent years, there has been increasing interest in the conversion of images into audio descriptions. This is a field that lies at the intersection of Computer Vision (CV) and Natural Language Processing (NLP), and it involves various tasks, including creating textual descriptions of images and converting them directly into auditory representations. Another aspect of this field is the synthesis of natural speech from text. This has significant potential to improve accessibility, user experience, and the applications of Artificial Intelligence (AI). In this article, we reviewed a wide range of image-to-audio conversion techniques. Various aspects of image captioning, speech synthesis, and direct image-to-speech conversion have been explored, from fundamental encoder–decoder architectures to more advanced methods such as transformers and adversarial learning. Although the focus of this review is on synthesizing audio descriptions from visual data, the reverse task of creating visual content from natural language descriptions is also covered. This study provides a comprehensive overview of the techniques and methodologies used in these fields and highlights the strengths and weaknesses of each approach. The study emphasizes the importance of various datasets, such as MS COCO, LibriTTS, and VizWiz Captions, which play a critical role in training models, evaluating them, promoting inclusivity, and solving real-world problems. The implications for the future suggest the potential of generating more natural and contextualized audio descriptions, whereas direct image-to-speech tasks provide opportunities for intuitive auditory representations of visual content.

Ear detection represents one of the key components of contemporary ear recognition systems. While significant progress has been made in the area of ear detection over recent years, most of the improvements are direct results of advances in the field of visual object detection. Only a limited number of techniques presented in the literature are domain–specific and designed explicitly with ear detection in mind. In this paper, we aim to address this gap and present a novel detection approach that does not rely only on general ear (object) appearance, but also exploits contextual information, i.e., face–part locations, to ensure accurate and robust ear detection with images captured in a wide variety of imaging conditions. The proposed approach is based on a Contex t–aware ${E}$ ar ${D}$ etection Net work (ContexedNet) and poses ear detection as a semantic image segmentation problem. ContexedNet consists of two processing paths: i) a context–provider that extracts probability maps corresponding to the locations of facial parts from the input image, and ii) a dedicated ear segmentation model that integrates the computed probability maps into a context–aware segmentation-based ear detection procedure. ContexedNet is evaluated in rigorous experiments on the AWE and UBEAR datasets and shown to ensure competitive performance when evaluated against state–of–the–art ear detection models from the literature. Additionally, because the proposed contextualization is model agnostic, it can also be utilized with other ear detection techniques to improve performance.