Preserving Privacy in Video Analytics: A Comprehensive Review of Face De-Identification and Background Blurring Techniques

Abstract

<p>The landscape of privacy protection within the domain of video analytics is in a constant state of evolution, driven by advances in computer vision and machine learning techniques. These advancements provide increasingly sophisticated video analysis capabilities, but they also raise the stakes in terms of personal privacy. In this review, we have conducted a thorough exploration into the intersection of video analytics and privacy preservation, focusing on two core techniques: face de-identification and background blurring. We rigorously analyze the latest advancements in face de-identification, including the nuanced approaches involving facial feature perturbation, deep learning methodologies, and the intriguing domain of generative adversarial networks, meticulously highlighting both their efficacy and inherent limitations. Simultaneously, we research numerous background blurring techniques, showcasing their remarkable ability to obscure contextual information while preserving the essential elements of the visual scene. This paper underscores the practical significance of these techniques through real-world applications in surveillance and the dynamic landscape of social platforms. In essence, our comprehensive review combines the multifaceted aspects of face de-identification and background blurring within the sphere of video analytics, thereby offering a nuanced and thorough understanding of this pivotal domain.</p>