Face detection hindering

Abstract

In this paper, we develop a face detection hindering method, as a means of preventing the threats to people's privacy, automatic video analysis may pose. Face detection in images or videos is the first step in human-centered video analysis to be followed, e.g. by automatic face recognition. Therefore, by hindering face detection, we also render automatic face recognition improbable. To this end, we examine the application of two methods. First, we consider a naive approach, i.e., we simply use additive or impulsive noise to the input image, until the point where the face cannot be automatically detected anymore. Second, we examine the application of the SVD-DID face de-identification method. Our experimental results denote that both methods attain high face detection failure rates.

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

“…Chriskos et al [158] described an transformation-based B-PET designed for hindering automatic face detection. The proposed face-detection hindering technique (as called by the authors) introduces artifacts into face images (e.g., noise and projections) that impair face detection, but still preserves enough information, so that faces remain intelligible for human observers.…”

Challenges in Autonomous UAV Cinematography: An Overview

“…Therefore, compliance with data privacy legislation is an issue that must be taken into account in autonomous UAV shooting scenarios. Privacy protection methods for face deidentification [18] [19], face detection obfuscation algorithms [20], or even soft/non biometric identifiers (e.g., tattoos, skin marks etc.) protection methods [21], can be employed to ensure privacy legislation compliance.…”

Can you Find a Face in a HEVC Bitstream?

“…We can scramble transform coefficients over the entire image, without knowing beforehand where the faces are. Due to scrambling, conventional face detectors' performance is hindered, similar to [10]. But because our face localization relies on HEVC syntax and not on pixel values, our method can still find faces in scrambled images, without being able to recognize them.…”

“…Privacyfriendly visual analytics are becoming increasingly important with the growth of public awareness of the widespread use of private data for commercial (and sometimes illegal) purposes. A recent proposal on this topic [10] advocates modifying the face region in an image in order to hinder face detection and thereby also hinder face recognition. Our approach is different.…”