A method may include receiving, by a virtual assistant of a user device, an input from a user, the virtual assistant being based on software. The method may include obtaining, by the virtual assistant of the user device and via a sensor of the user device, audio information or video information of the user. The method may include determining, by the virtual assistant of the user device, an identity of the user based on the audio information or the video information of the user and a set of facial embeddings and speech embeddings that is correlated with the user, the set of facial embeddings and speech embeddings being generated using a facial embedding model, a speech embedding model, and a sound source localization model. The method may include performing, by the virtual assistant of the user device, an action based on the input and the identity of the user.

Systems and methods for determining dwell time is provided. The method includes receiving images of an area including one or more people from one or more cameras, and detecting a presence of each of the one or more people in the received images using a worker. The method further includes receiving by the worker digital facial features stored in a watch list from a master controller, and performing facial recognition and monitoring the dwell time of each of the one or more people. The method further includes determining if each of the one or more people is in the watch list or has exceeded a dwell time threshold.

A process and an image evaluation system are provided with a mobile sensor arrangement, including a camera (IR), a motion sensor (IMU), and a receiver (Komm), that is moved through a spatial area. The camera generates an image sequence. The motion sensor generates an orientation signal with camera viewing direction in a predefined three-dimensional coordinate system when generating an image. A signal processing unit (Sv) checks whether the receiver is receiving a signal from a transmitter (UWB.1, UWB.2, UWB.3) of a transmitter group. If the receiver receives a signal, the signal processing unit determines the distance between the transmitter and the receiver. A classifier (Kl) searches for images of humans in images of the image sequence. The signal processing unit decides whether an image of a human shows a person associated with a transmitter of the transmitter group and may generate a trajectory describing the movement of the camera.

Systems and methods for identification (ID) document verification using hybrid near-field communications (NFC) authentication and optical authentication are provided. An exemplary method includes receiving, by a client device, an image of an ID document. Based on the image of the ID document, a determination is made whether the ID document includes a near-field communications (NFC) chip that stores data comprising identifying information for an owner of the identification. Based on this determination of whether the ID document includes an NFC chip, the ID document is verified by selectively using at least one of NFC chip authentication and optical authentication, to obtain a verification result.

Systems and methods for identification (ID) document verification using hybrid near-field communications (NFC) authentication and optical authentication are provided. An exemplary method includes receiving, by a client device, an image of an ID document. Based on the image of the ID document, a determination is made whether the ID document includes a near-field communications (NFC) chip that stores data comprising identifying information for an owner of the identification. Based on this determination of whether the ID document includes an NFC chip, the ID document is verified by selectively using at least one of NFC chip authentication and optical authentication, to obtain a verification result.

This application discloses a method for training a gaze tracking model, including: obtaining a training sample set; processing the eye sample images in the training sample set by using an initial gaze tracking model to obtain a predicted gaze vector of each eye sample image; determining a model loss according to a cosine distance between the predicted gaze vector and the labeled gaze vector for each eye sample image; and iteratively adjusting one or more reference parameters of the initial gaze tracking model until the model loss meets a convergence condition, to obtain a target gaze tracking model. According to the solution provided in this application, a gaze tracking procedure is simplified, a difference between a predicted value and a labeled value can be better represented by using the cosine distance as a model loss to train a model, to improve prediction accuracy of the gaze tracking model.

This application discloses a method for training a gaze tracking model, including: obtaining a training sample set; processing the eye sample images in the training sample set by using an initial gaze tracking model to obtain a predicted gaze vector of each eye sample image; determining a model loss according to a cosine distance between the predicted gaze vector and the labeled gaze vector for each eye sample image; and iteratively adjusting one or more reference parameters of the initial gaze tracking model until the model loss meets a convergence condition, to obtain a target gaze tracking model. According to the solution provided in this application, a gaze tracking procedure is simplified, a difference between a predicted value and a labeled value can be better represented by using the cosine distance as a model loss to train a model, to improve prediction accuracy of the gaze tracking model.

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

The human body 3D key point detection method includes: acquiring a to-be-detected image, the to-be-detected image including first human body image data; inputting the to-be-detected image into a key point extraction model to acquire N first 3D heatmaps of N human body 3D key points in the first human body image data, each first 3D heatmap representing a Gaussian distribution of one human body 3D key point in first human body image data in a predetermined space, where N is a positive integer; and determining coordinate information about the N human body 3D key points in the first human body image data based on the N first 3D heatmaps.