FRAMEWORK FOR IDENTITY PRIVACY THROUGH GENDER BASED SKELETONIZATION
Harrison Hwang · 2025
The protection of one’s privacy and sensitive information is becoming increasingly difficult in the modern age full of surveillance and data collection. Through the use of image based object detection machine learning models trained for human and facial recognition, people can be identified and tracked to a terrifyingly accurate degree. On the other hand, the information present in surveillance media can play a key role in security and law enforcement. This presents a problem of how to preserve key information without compromising the privacy of any individuals present in the video. In this research project, Computer Vision techniques and a collection of different machine learning models are used to replace the bodies of people present in a video with a gendered skeleton representation. The Ultralytics YOLO CNN object detection model is used to detect the people in the video. The DeepSORT Deep Learning object tracking model is used to accurately track and assign unique ids to each person. An open source HuggingFace CNN gender detection model is used to assign a gender to each detected person. Finally the Google Mediapipe pose landmark detection model is used to generate a skeleton representation of each detected person. Using this technique, personally identifiable features such as their facial features, skin tone, clothes, etc. can be hidden while preserving gender and movement data.