Human Detection Using Biological Signals in Camera Images with Privacy Aware

In the Internet of Things (IoT), in which people sensing occurs everywhere, cameras are being increasingly used because they provide inexpensive and effective sensing devices. However, while cameras can provide significant amounts of information, much of that information is personal and there are significant concerns that individual privacy could be compromised. Furthermore, since home appliances are increasingly being connected to the Internet via the IoT, it has become possible for user images to leak out unintentionally. With these concerns in mind, we propose a human detection method that protects user privacy by using intentionally blurred images. In this method, the presence of a human being is determined by dividing an image into several regions and then calculating the heart rate detected in each region. In our performance evaluation, the proposed method showed dominant performance results when compared with an OpenCV-based face detection method, and was confirmed to be an effective method for detecting human beings in both normal and blurred images.