A physical access control (PAC) system configured to perform a two-factor authentication prior to granting access to a secure area. The PAC system includes an access point device configured to perform facial recognition on a person proximate to the access point device, and perform wireless handshake with a mobile device associated with the person prior to granting or denying entry to the secure area.

The present subject matter describes a method to detect anomaly in an environment based on AI techniques. The method comprises receiving one or more data representations of one or more objects present in an environment. A first-type of information is captured from a first-area within the one or more data representations. A second-type of information from a second-area different than the first area in the data representations is also captured. A third information is generated from the first information, said third information corresponding to predicted information for the second area using one or more artificial-intelligence models for evaluating the second information. The third information is compared with the second information to determine abnormality with respect to state or operation of one or more objects within the environment.

The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

A method of adjusting mental state includes acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data, identifying a first set of features, predicting a baseline mental state, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The baseline mental state is predicted based on the first set of features. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. An indication of the one or more actions of the predicted path is output to the individual.

The present disclosure provides tracking systems and methods. One such method comprises generating a continuous wave signal over a frequency range; applying the continuous wave signal to a reading coil element that is electromagnetically coupled to a sensor array, wherein the sensor array comprises a plurality of sensing resonators tuned at different resonance frequencies, where an output frequency response of the sensor array varies as a function of a location of a target object or a shape of the target object within a coverage area of the sensor array; acquiring frequency spectrum data showing changes in the output frequency response of the sensor array from the reading coil element; and predicting, by a control unit device using machine learning, a location of the target object within the coverage area or a behavior of the target object based on the acquired frequency spectrum data. Other methods and systems are also provided.

A method for detecting a parallax problem in sensor data from two sensors, wherein the sensors are spaced apart at different positions and at least partly capture the same environment, and one of the sensors provides distance information items. The method includes receiving the acquired sensor data from the sensors; receiving or estimating sensor visual range information items from the other of the sensors; assigning measured values in the acquired sensor data of the one sensor to measured values corresponding to each of them from the other sensor, the assignment taking into account respective imaging conditions; comparing the distance information items and the received or estimated sensor visual range information items on each of the measured values of the sensor data, wherein a parallax problem is detected in response to a distance exceeding a threshold criterion; and outputting a comparison result.

Methods, devices and non-transitory computer-readable storage medium for processing a sequence of respective top view images of a same terrestrial location are provided. One of the method may comprise choosing one image, called reference image, among the respective top view images, estimating for each respective top view image a respective geometric deformation between the respective top view image and the reference image, computing by the respective geometric deformations respective subpixel positions of the respective top view images relative to one high-resolution coordinate system, interpolating at the respective subpixel positions to sample at least part of at least some of the respective top view images on a prescribed grid to obtain a high-resolution image.

Provided are methods and apparatuses for positioning face feature points. The method includes: carrying out edge detection on a face image to obtain a face feature line image; and fusing the face image and the face feature line image to obtain position information of face feature points.

A deep learning machine includes a classification unit having a labeling criterion and configured to label input data according to the labeling criterion, a conversion unit configured to integerize input data labeled as a first type requiring integerization among the input data labeled by the classification unit, a first learning data unit configured to receive the input data of the first type integerized through the conversion unit and to infer output data, and a second learning data unit configured to receive input data labeled as a second type requiring no integerization and to infer the output data.

A biological-information detection device includes a video input section accepting video signals including three wavelength components in an infrared region of reflected light from an object, a wavelength detecting section acquiring a wavelength and an intensity of the reflected light from the video signals, a face feature amount detecting section detecting a plurality of feature points of a face based on the video signals, a measurement target area identifying section identifying a measurement target area on a basis of the plurality of feature points of the face detected, a wavelength fluctuation detecting section detecting a difference between a wavelength of reflected light from the measurement target area at a certain point in time and a wavelength of reflected light at a point in time preceding the certain point in time, and a pulse wave detecting section detecting a change in the detected difference according to the point in time.