An apparatus and a method with image recognition-based security are disclosed. The method includes, for an unlocked terminal, tracking a face detected in a previous frame, detecting a background region change between the previous frame and a current frame based on a region of the tracked face, when the background region change is not detected, determining whether a state maintenance time fails to meet a preset time, in response to the state maintenance time failing to meet the preset time, determining an operation mode to be a first operation mode for determining whether recognition succeeds for the current frame, performing the first operation mode, including performing face detection with respect to the current frame, and maintaining the unlocked state of the terminal for the current frame when the face is detected as a result of the performing of the face detection, representing that the recognition succeeded for the current frame.

The present invention is directed to systems and process for detecting a febrile condition in screening of multiple or large groups of persons for the likelihood of infectious diseases. The system comprises a temperature measurement device having at least a processor, a display, temperature sensor, camera module, and facial recognition software. The display may include a positional overlay. The system further includes a database containing baseline temperatures for a number of persons, including baseline temperatures by age group and by gender. An input device optionally may identify the specific person or provide survey questions to a person to further determine a febrile condition. Based on the baseline temperatures, measured temperature and other characteristics of the person, a febrile condition may be determined. Additionally, the system may include a secondary screening component providing nasal swabbing or saliva testing.

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.

The present disclosure provides a verification system. The verification system is formed with a trusted execution environment, the verification system includes a processor set, and the processor set is configured to: obtain an infrared image to be verified of a target object; determine, in the trusted execution environment, whether the infrared image to be verified matches a pre-stored infrared template; in response to determining that the infrared image to be verified matches the pre-stored infrared template, obtain a depth image to be verified of the target object; and determine, in the trusted execution environment, whether the depth image to be verified matches a pre-stored depth template.

The present invention relates to a method for conducting a videoconference including a certified verification of at least one communication partner (first participant). Thus, another communication partner (second participant) is provided with legal certainty that the person whom he or she talks to is indeed the person she says she is. For this purpose, on the side of the first participant a technical verification step is performed in advance of setting up the video call, which is based at least on a comparison of an image of the first participant captured in real-time and an image available on an official document such as an ID card of the first participant. In case of success, respective verified identification information of the first participant is transmitted to the terminal equipment of the second participant and indicated to the second participant.

The present disclosure provides a photo album management method. The method includes obtaining voice search information from a user, performing intent recognition on the voice search information to obtain an intent recognition result which indicates an intent of the user for a photo album, obtaining a voiceprint feature from the voice search information to determine identity information of the user, sending the intent recognition result and the identity information of the user, and opening the photo album according to the intent recognition result and the identity information.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A method for driver identification including recording a first image of a vehicle driver; extracting a set of values for a set of facial features of the vehicle driver from the first image; determining a filtering parameter; selecting a cluster of driver identifiers from a set of clusters, based on the filtering parameter; computing a probability that the set of values is associated with each driver identifier of the cluster; determining, at the vehicle sensor system, driving characterization data for the driving session; and in response to the computed probability exceeding a first threshold probability: determining that the new set of values corresponds to one driver identifier within the selected cluster, and associating the driving characterization data with the one driver identifier.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

A system for authenticating a user attempting to access a computing device or a software application executing thereon. A data storage device stores one or more digital images or frames of video of face(s) of authorized user(s) of the device. The system subsequently receives from a first video camera one or more digital images or frames of video of a face of the user attempting to access the device and compares the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device. To ensure the received video of the face of the user attempting to access the device is a real-time video of that user, and not a forgery, the system further receives a first photoplethysmogram (PPG) obtained from a first body part (e.g., a face) of the user attempting to access the device, receives a second PPG obtained from a second body part (e.g., a fingertip) of the user attempting to access the device, and compares the first PPG with the second PPG. The system authenticates the user attempting to access the device based on a successful comparison of (e.g., correlation between, consistency of) the first PPG and the second PPG and based on a successful comparison of the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device.