Biblio

An enormous number of images are currently shared through social networking services such as Facebook. These images usually contain appearance of people and may violate the people's privacy if they are published without permission from each person. To remedy this privacy concern, visual privacy protection, such as blurring, is applied to facial regions of people without permission. However, in addition to image quality degradation, this may spoil the context of the image: If some people are filtered while the others are not, missing facial expression makes comprehension of the image difficult. This paper proposes an image melding-based method that modifies facial regions in a visually unintrusive way with preserving facial expression. Our experimental results demonstrated that the proposed method can retain facial expression while protecting privacy.

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. At the same time, there is an increasing need to share such video data across a wide spectrum of stakeholders including professionals, therapists and families facing similar challenges. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this paper, we propose a method of manipulating facial expression and body shape to conceal the identity of individuals while preserving the underlying affect states. The experiment results demonstrate the effectiveness of our method.