People sensing occurs everywhere, in the Internet of Things (IoT). Cameras are being increasingly used because they provide inexpensive and effective sensing devices. However, the camera acquires the information that identifies an individual, there is a problem that the privacy of the person is invaded. 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 face 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 existing face detection method, and was confirmed to be an effective method for detecting faces in both normal and blurred images. We confirmed the influence of the performance in the proposed method when changing the sharpness of the images. The proposed method also showed high accuracy position detection performance results. Furthermore, we confirmed the effectiveness of the proposed method in near‐infrared images and distorted images.
The remote measurement of the heart rate while the user is watching TV can bring many promising applications to our home such as user's preference estimation, automatic digest edition and fitness. We developed a non-contact heart rate measurement method using camera image so that users can measure the heart rate without any attachments. We focused attention on the blood oxygenated hemoglobin which absorbs the green light and measures the heart rate by detecting the variation of the green color intensity of a person's face image. In previous research, it was not possible to measure the heart rate with precision when a person was in movement. In this proposed method, we can expect that it will be applied to various applications since we can accurately measure the heart rate even while moving the face. The results showed a performance of detection error smaller than 3 bpm for lightning conditions over 500 lux and distances up to 6.0 m.
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