Abstract: A device of contactless physiological measurement with error compensation function is disclosed, and comprises a camera and a modular electronic device that is embedded with a face detection unit, a physiological parameter estimating unit, a feature extraction unit, an error compensation unit, and a physiological parameter generating unit therein. The face detection unit detects a face region from a user image, such that the physiological parameter estimating unit extracts an rPPG signal from the face region, so as to calculate a preliminary physiological parameter based on the rPPG signal. Simultaneously, the feature extraction unit extracts an error feature from the face region, such that the error compensation unit generates an error compensation parameter based on the error feature and the preliminary physiological parameter.

Abstract: A contactless physiological measurement device is disclosed. The contactless physiological measurement device is an electronic device comprising a processor and a memory storing an application program. When the application program is executed, the processor controls a front camera and a rear camera of the electronic device to photograph a user, so as to obtain a face image and a hand image. Subsequently, after extracting a first rPPG signal and a second rPPG signal from the face image and the hand image respectively, physiological parameters are calculated by apply a signal process to the first/second rPPG signal. Moreover, a signal parameter difference is also acquired after applying a signal difference calculation to the two rPPG signals. Consequently, an estimation value of blood pressure is outputted by inputting user anthropometric parameter, said signal parameter difference and at least one physiological parameter into a pre-trained blood pressure estimating model.

Abstract: The present invention provides a physiological information detection method for calculating a physiological value by using changes of a dynamic image. The detection method includes: acquiring detection data from a gray-scale value of the dynamic image, and transforming the detection data into frequency data. The detection method further includes: determining whether the frequency data meet a preset condition, and using a transformation model of a corresponding transformation combination accordingly to transform the frequency data into a physiological value. The present invention further provides a physiological information detection device applying the detection method.

Abstract: A people-flow analysis system includes an image source, a computing device, and a host. The image source captures a first image and a second image. The computing device is connected to the image source. The computing device identifies the first image according to a data set to generate a first detecting image. The first detecting image has a position box corresponding to a pedestrian in the first image. The computing device generates a tracking image according to the data set and a difference between the first detecting image and the second image. The tracking image has another position box corresponding to a pedestrian in the second image. The host is connected to the computing device and generates a people-flow list according to the first detecting image and the tracking image.

Abstract: The present invention provides a physiological information detection method for calculating a physiological value by using changes of a dynamic image. The detection method includes: acquiring detection data from a gray-scale value of the dynamic image, and transforming the detection data into frequency data. The detection method further includes: determining whether the frequency data meet a preset condition, and using a transformation model of a corresponding transformation combination accordingly to transform the frequency data into a physiological value. The present invention further provides a physiological information detection device applying the detection method.

Abstract: A non-contact heartbeat rate measurement system includes an image sensor, a target region selecting module, a heartbeat signal calculating module, a spectrum analyzing module, a vibration detecting module, a heartbeat peak selecting module. When a signal quality indicator is greater than a threshold value, and a first peak with global highest signal intensity in a heartbeat spectrum is similar to a face vibration frequency, the heartbeat peak selecting module selects a second peak with local highest signal intensity from a part frequency band of the heartbeat spectrum as an output heartbeat frequency. A non-contact heartbeat rate measurement method and an apparatus are disclosed herein.

Abstract: A drunken driving judgment system includes an image capturing module configured to obtain multiple images associated with a subject; a physiological parameter computing module coupled to the image capturing module, and configured to generate at least one physiological parameter according to the multiple images associated with the subject, wherein the at least one physiological parameter comprises at least one of a Remote PhotoPlethysmoGraphy, a heart rate, a heart rate variability, a blood oxygen, a breath rate, and a blood pressure; and an alcohol detection calculation unit coupled to the physiological parameter computing module, and configured to generate a drunken driving judgment result according to the at least one physiological parameter to indicate whether the subject is drunken.

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 present invention provides a contactless-type sport training monitor method, comprising: selecting at least an image database to recognize a plurality of expressions in the image database; making pre-processing for the plurality of expressions: using a convolutional neural network as a feature point extraction model; acquiring a human image; tracking a first target region and a second target region in the human image; making chrominance-based rPPG trace extraction; using the deep level model to compare the second target region image; and to calculate a post-exercise heart rate recovery achievement ratio, to judge the Rating of Perceived Exertion, and judging whether the human body is under overtraining status or not.

Abstract: A facial expression recognition training system includes a training module, feature database, a capturing module, a recognition module and an adjusting module. The training module trains a facial expression feature capturing model according to known face images. The feature database stores known facial expression features of the known face images. The capturing module continuously captures first face images, and the facial expression feature capturing model outputs facial expression features of the first face images according to the first face images. The recognition module compares the facial expression features and the known facial expression features, and fit the facial expression features to the first known facial expression features that is one kind of the known facial expression feature accordingly. The adjusting module adjusts the facial expression feature capturing model to reduce the differences between the facial expression features and the known facial expression features.

Abstract: The present invention provides an image based blood pressure monitoring method, comprising: acquiring at least a human image information of at least a human skin area; according to the human image information to locate at least a ROI; extracting the human image information of the ROI, and calculate; filtering the average value of the human image information; monitoring the filtered signal; calculating an image pulse transmit time of the filtered signal, and calculating an inter-beat interval of the filtered signal; and employ the specific prediction model, to calculate a systolic pressure value and a diastolic pressure value.

Abstract: A people-flow analysis system includes an image source, a computing device, and a host. The image source captures a first image and a second image. The computing device is connected to the image source. The computing device identifies the first image according to a data set to generate a first detecting image. The first detecting image has a position box corresponding to a pedestrian in the first image. The computing device generates a tracking image according to the data set and a difference between the first detecting image and the second image. The tracking image has another position box corresponding to a pedestrian in the second image. The host is connected to the computing device and generates a people-flow list according to the first detecting image and the tracking image.

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.

Abstract: A non-contact heartbeat rate measurement system includes an image sensor, a target region selecting module, a heartbeat signal calculating module, a spectrum analyzing module, a vibration detecting module, a heartbeat peak selecting module. When a signal quality indicator is greater than a threshold value, and a first peak with global highest signal intensity in a heartbeat spectrum is similar to a face vibration frequency, the heartbeat peak selecting module selects a second peak with local highest signal intensity from a part frequency band of the heartbeat spectrum as an output heartbeat frequency. A non-contact heartbeat rate measurement method and an apparatus are disclosed herein.

Abstract: The present invention provides a contactless-type sport training monitor method, comprising: selecting at least an image database to recognize a plurality of expressions in the image database; making pre-processing for the plurality of expressions: using a convolutional neural network as a feature point extraction model; acquiring a human image; tracking a first target region and a second target region in the human image; making chrominance-based rPPG trace extraction; using the deep level model to compare the second target region image; and to calculate a post-exercise heart rate recovery achievement ratio, to judge the Rating of Perceived Exertion, and judging whether the human body is under overtraining status or not.

Abstract: The present invention provides an image based blood pressure monitoring method, comprising: acquiring at least a human image information of at least a human skin area; according to the human image information to locate at least a ROI; extracting the human image information of the ROI, and calculate; filtering the average value of the human image information; monitoring the filtered signal; calculating an image pulse transmit time of the filtered signal, and calculating an inter-beat interval of the filtered signal; and employ the specific prediction model, to calculate a systolic pressure value and a diastolic pressure value.

Abstract: A facial expression recognition training system includes a training module, feature database, a capturing module, a recognition module and an adjusting module. The training module trains a facial expression feature capturing model according to known face images. The feature database stores known facial expression features of the known face images. The capturing module continuously captures first face images, and the facial expression feature capturing model outputs facial expression features of the first face images according to the first face images. The recognition module compares the facial expression features and the known facial expression features, and fit the facial expression features to the first known facial expression features that is one kind of the known facial expression feature accordingly. The adjusting module adjusts the facial expression feature capturing model to reduce the differences between the facial expression features and the known facial expression features.

Abstract: The present invention provides an imaging-type heart rate monitoring device and method thereof. It acquires the human image information tracks the target area in the human image information, and extracts the target image in the target area. It transforms the human image information through the first frequency domain transform. It filters the residual noise of the continuous wavelet transform information. It rebuilds the residual noise filtered information by the continuous wavelet transform. It transforms the continuous wavelet transform rebuilt information by the Fourier transform. It obtains a heart rate value through the second frequency domain transformed information.