Abstract: A method for detecting a face synthetic image, an electronic device and a storage medium are provided. The technical solution includes inputting a face image to be detected into a pre-trained convolution neural network to obtain a raw image feature of the face image; inputting the raw image feature into a first full connected layer and a second full connected layer respectively to obtain a first feature vector corresponding to a face key point of the face image and a second feature vector corresponding to the face image; merging the first feature vector and the second feature vector to obtain a merged feature vector; inputting the merged feature vector to a third full connected layer to obtain a detection result of the face image.

Abstract: A method and an apparatus for measuring temperature, and a computer-readable storage medium includes detecting a target position of an object in an input image; determining key points of the target position and weight information of each key point based on a detection result of the target position, in which the weight information is configured to indicate a probability of each key point being covered; acquiring temperature information of each key point; and determining a temperature of the target position at least based on the temperature information and the weight information of each key point.

Abstract: A target part tracking method and apparatus, an electronic device and a computer-readable storage medium, which relate to the field of artificial intelligence, and specifically relate to computer vision. Said method may comprise: on the basis of a previous detection area for a target part of an object in a previous frame of a video, determining a current detection area for detecting the target part in a current frame of the video (302). The method further comprises: determining the probability of the target part being within the current detection area (304). In addition, the method may further comprise: in response to the probability being greater than or equal to a predetermined threshold, determining a subsequent detection area for detecting the target part in a subsequent frame of the video at least on the basis of the current detection area and the previous detection area (306).

Abstract: Provided are a method and apparatus for recognizing a dangerous driving behavior, an electronic device and a storage medium. The method is described below. A to-be-recognized image is input to a pre-trained human face detection model, human face detection is performed on the to-be-recognized image through the pre-trained human face detection model, and a human face detection frame of the to-be-recognized image is obtained; and the human face detection frame is input to a pre-trained dangerous driving behavior recognition model, dangerous driving behavior recognition is performed on the human face detection frame through the pre-trained dangerous driving behavior recognition model, and a dangerous driving behavior recognition result corresponding to the human face detection frame is obtained.

Abstract: A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

Abstract: A method, an electronic device, and a storage medium are disclosed.

Abstract: Provided are an abnormal driving behavior determination method and apparatus, a device, a vehicle and a medium, which relate to the field of artificial intelligence and, in particular, to the field of computer vision and in-depth learning. A specific implementation includes: determining multiple arm keypoints of an arm included in an original image of a driving user; determining an arm bending angle of the driving user according to the multiple arm keypoints; and determining whether the driving user has an abnormal driving behavior according to the arm bending angle. In the present application, the arm bending angle of the driving user is introduced for characterizing a driving pose of the driving user so that the abnormal driving behavior of the driving user is determined according to the arm bending angle, providing a new manner for determining the abnormal driving behavior.

Abstract: A method of face anti-spoofing, a device and a storage medium are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology, and can be applied in a face recognition scene. The method includes: acquiring a face image sequence including a plurality of face images; acquiring, based on the face image sequence, a corresponding eye image sequence including a plurality of eye images; recognizing, based on the eye image sequence, a color of an optical spot caused by a pupil in each eye image, so as to obtain a pupil color recognition result; and determining a face anti-spoofing result by using the face image in the face image sequence, in response to the pupil color recognition result indicating that the face image sequence is a sequence of face images captured on site after a face recognition process is started.

Abstract: A method and apparatus for waking up a device, an electronic device, and a storage medium are provided, which are related to fields of image processing and deep learning. The method includes: acquiring an environment image of a surrounding environment of a target device in real time, and recognizing a face region of a user in the environment image; acquiring a plurality of facial landmarks in the face region, and acquiring a left eye image and a right eye image according to the facial landmarks; acquiring a left eye sight classification result and a right eye sight classification result according to the left eye image and the right eye image; and waking up the target device in a case of determining that the user is looking at the target device according to the left eye sight classification result and the right eye sight classification result.

Abstract: Provided are an abnormal driving behavior determination method and apparatus, a device, a vehicle and a medium, which relate to the field of artificial intelligence and, in particular, to the field of computer vision and in-depth learning. A specific implementation includes: determining multiple arm keypoints of an arm included in an original image of a driving user; determining an arm bending angle of the driving user according to the multiple arm keypoints; and determining whether the driving user has an abnormal driving behavior according to the arm bending angle. In the present application, the arm bending angle of the driving user is introduced for characterizing a driving pose of the driving user so that the abnormal driving behavior of the driving user is determined according to the arm bending angle, providing a new manner for determining the abnormal driving behavior.

Abstract: Disclosed are a method and apparatus for detecting an abnormal driving posture, a device, a vehicle and a medium. The specific solution is described below. A human body key point is determined according to an acquired original image of a driving user during driving, where the human body key point includes a shoulder key point; and an abnormal driving posture of the driving user is detected according to the shoulder key point.

Abstract: A method and apparatus for spoof detection are provided. An implementation of the method includes: acquiring an original image; inputting the original image into a training-completed spoof cue extraction network, to obtain a spoof cue signal of the original image; calculating an element-wise mean value of the spoof cue signal; and generating a spoof detection result of the original image based on the element-wise mean value.

Abstract: The present disclosure provides a method for detecting a face synthetic image, an electronic device and a storage medium. The technical solution includes inputting a face image to be detected into a pre-trained convolution neural network to obtain a raw image feature of the face image; inputting the raw image feature into a first full connected layer and a second full connected layer respectively to obtain a first feature vector corresponding to a face key point of the face image and a second feature vector corresponding to the face image; merging the first feature vector and the second feature vector to obtain a merged feature vector; inputting the merged feature vector to a third full connected layer to obtain a detection result of the face image.

Abstract: A method and an apparatus for recognizing a wearing state of a safety belt are disclosed. The method includes: obtaining an image by monitoring a vehicle; performing face recognition on the image to obtain a face region; determining a target region from the image based on a size and a position of the face region; and recognizing a wearing state of a safety belt based on an image feature of the target region.