Enhancing Privacy in Computer Vision Applications: An Emotion Preserving Approach to Obfuscate Faces

Abstract

Computer vision offers many techniques to facilitate the extraction of semantic information from images. If the images include persons, preservation of privacy in computer vision applications is challenging, but undoubtedly desired. A common technique to prevent exposure of identities is to cover peoples’ faces with, for example, a black bar. Although emotions are crucial for reasoning in many applications, facial expressions may be covered, which hinders the recognition of actual emotions. Thus, recorded images containing obfuscated faces may be useless for further analysis and investigation. We introduce an approach that enables automatic detection and obfuscation of faces. To avoid privacy conflicts, we use synthetically generated faces for obfuscation. Furthermore, we reconstruct the facial expressions of the original face, adjust the color of the new face and seamlessly clone it to the original location. To evaluate our approach experimentally, we obfuscate faces from various datasets by applying blurring, pixelation and the proposed technique. To determine the success of obfuscation, we verify whether the original and the resulting face represent the same person using a state-of-the-art matching tool. Our approach successfully obfuscates faces in more than 97% of the cases. This performance is comparable to blurring, which scores around 96%, and even better than pixelation (76%). Moreover, we analyze how effectively emotions can be preserved when obfuscating the faces. For this, we utilize emotion recognizers to recognize the depicted emotions before and after obfuscation. Regardless of the recognizer, our approach preserves emotions more effectively than the other techniques while preserving a convincingly natural appearance.