Abstract: The present disclosure provides a non-contact heart rhythm category monitoring system, which includes steps as follows. Facial images are continuously captured through an image sensor; images of a continuous target area for a predetermined duration are extracted from the facial images; non-contact physiological signal related to heartbeats are captured from the images of the continuous target area; the non-contact physiological signal are classified into a normal heart rhythm, an atrial fibrillation and a non-atrial fibrillation arrhythmia.

Abstract: A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

Abstract: A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

Abstract: A method of liveness detection for a computing device comprises acquiring at least one full cycle of a remote photoplethysmography, rPPG, signal from a skin image, extracting at least one rPPG waveform characteristic from the full cycle of the rPPG signal, and determining whether the skin image includes a life according to the extracted rPPG waveform characteristic.

Abstract: Disclosures of the present invention describe a non-contact physiological signal measuring device comprising a light sensing unit and a signal processing module, wherein the light sensing unit is adopted for collecting a scattered light from a surface of a sensing portion of a subject. After receiving the scattered light by a signal receiving unit of the signal processing module, a signal processing unit can subsequently obtain physiological characteristic(s) after applying signal process to a physiological signal with respect to the scattered light. Particularly, this novel non-contact physiological signal measuring device does not include any one camera or image capturing unit, such that the subject is able to receive a physiological signal measurement in the case of having well a personal privacy protection as well as preventing the subject's skin from being hurt.

Abstract: A method of liveness detection for a computing device comprises acquiring at least one full cycle of a remote photoplethysmography, rPPG, signal from a skin image, extracting at least one rPPG waveform characteristic from the full cycle of the rPPG signal, and determining whether the skin image includes a life according to the extracted rPPG waveform characteristic.

Abstract: A recycling system and a method based on deep-learning and computer vision technology are disclosed. The system includes a trash sorting device and a trash sorting algorithm. The trash sorting device includes a trash arraying mechanism, trash sensors, a trash transfer mechanism and a controller. The trash arraying mechanism is configured to process trash in a batch manner. The controller drives the trash arraying mechanism according to the signals of trash sensors and controls the sorting gates of the trash sorting mechanism to rotate. The trash sorting algorithm makes use of the images of trash, wherein the images are taken by cameras in different directions. The trash sorting algorithm includes a dynamic object detection algorithm, an image pre-processing algorithm, an identification module and a voting and selecting algorithm. The identification module is based on the convolutional neural networks (CNNs) and may at least identify four kinds of trash.

Abstract: The invention is related to a method of three-dimensional face reconstruction by inputting a single face image to reconstruct a three-dimensional face model, therefore, the human face image is seen at various angles of three-dimensional face through rotating the model images.

Abstract: The invention is related to a method of three-dimensional face reconstruction by inputting a single face image to reconstruct a three-dimensional face model, therefore, the human face image is seen at various angles of three-dimensional face through rotating the model images.

Abstract: A recycling system and a method based on deep-learning and computer vision technology are disclosed. The system includes a trash sorting device and a trash sorting algorithm. The trash sorting device includes a trash arraying mechanism, trash sensors, a trash transfer mechanism and a controller. The trash arraying mechanism is configured to process trash in a batch manner. The controller drives the trash arraying mechanism according to the signals of trash sensors and controls the sorting gates of the trash sorting mechanism to rotate. The trash sorting algorithm makes use of the images of trash, wherein the images are taken by cameras in different directions. The trash sorting algorithm includes a dynamic object detection algorithm, an image pre-processing algorithm, an identification module and a voting and selecting algorithm. The identification module is based on the convolutional neural networks (CNNs) and may at least identify four kinds of trash.