Privacy-aware deep learning for gender detection

• Main Author: Lee, Craigdon Zhi Jie
• Other Authors: Tay, Wee Peng
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering; Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/150349
• Institution: Nanyang Technological University

With the recent advancements made in deep learning, it is clear that deep learning has become the most promising approach in artificial intelligence to tackle complex problems. Deep learning has shown its prowess in being able to learn large amounts of features due to its substantial learning capacity. This report is a documentation of the progress of a Final Year Project. The aim is to Incorporate Generative Adversarial Privacy to achieve Gender neutrality of a face image coupled with A skin disease identifier created using YOLO. Being able to preserve the patient's identity while identifying a skin disease. So as to encourage patients to use medical application systems with heightened privacy and also to give a second opinion on common skin diseases.