An artificial-reality system comprising (1) a wearable dimensioned to be donned on a body part of a user, wherein the wearable comprises (A) a set of electrodes that detect one or more neuromuscular signals via the body part of the user and (B) a transmitter that transmits an electromagnetic signal, (2) a head-mounted display communicatively coupled to the wearable, wherein the head-mounted display comprises a set of receivers that receive the electromagnetic signal, and (3) one or more processing devices that (1) determine, based at least in part on the neuromuscular signals, that the user has made a specific gesture and (2) determine, based at least in part on the electromagnetic signal, a position of the body part of the user when the user made the specific gesture. Various other apparatuses, systems, and methods are also disclosed.

Embodiments herein disclose computer-implemented methods, computer program products and computer systems for authenticating a user. The computer-implemented method may include receiving biographical data corresponding to a user. A change rate may be determined based on user biographical data. The computer-implemented method may include receiving first biometric data having a time-varying characteristic from the user at a first time and receiving second biometric data having the time-varying characteristic from the user at a second time that is later in time than the first time. Further, the computer-implemented method may include determining third biometric data based at least on the first biometric data, the second time, and the time-varying characteristic, and authenticating the user if the third biometric data is within a predetermined threshold of the second biometric data at the second time.

The technology disclosed can provide methods and systems for identifying users while capturing motion and/or determining the path of a portion of the user with one or more optical, acoustic or vibrational sensors. Implementations can enable use of security aware devices, e.g., automated teller machines (ATMs), cash registers and banking machines, other secure vending or service machines, security screening apparatus, secure terminals, airplanes, automobiles and so forth that comprise sensors and processors employing optical, audio or vibrational detection mechanisms suitable for providing gesture detection, personal identification, user recognition, authorization of control inputs, and other machine control and/or machine communications applications. A virtual experience can be provided to the user in some implementations by the addition of haptic, audio and/or other sensory information projectors.

The present invention provides a portable terminal (10) that includes an acquisition unit (11) that acquires a user image including a user, and a screen generation unit (12) that changes a position of an operation button on a screen to be displayed on a touch panel display (14), according to whether a hand of the user included in the user image is a right hand or a left hand.

An object pose and model estimation method includes acquiring a global feature of an input image, and a location code of an object including location information for a joint point of the object and location information for a model vertex in a template model; determining a local area feature of the object based on the global feature of the input image and based on the location code of the object in the template model; and acquiring location information for the joint point of the object in the input image and location information for the model vertex in the input image based on the local area feature of the object.

Neutral avatars are neutral with reference physical characteristics of the corresponding user, such as weight, ethnicity, gender, or even identity. Thus, neutral avatars may be desirable to use in various copresence environments where the user desires to maintain privacy with reference to the above-noted characteristics. Neutral avatars may be configured to convey, in real-time, actions and behaviors of the corresponding user without using literal forms of the user's actions and behaviors.

A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.

An exemplary method includes a gesture recognition system determining, based on imagery of a user while the user touchlessly interacts with a user interface of a computer system, a configuration of a plurality of landmarks associated with the user and comparing the configuration of the plurality of landmarks to defined landmark configurations associated with a plurality of defined gestures. Each of the plurality of defined gestures may be associated with a different user input enterable by way of the user interface of the computer system. The method may further include the gesture recognition system selecting, based on the comparing of the configuration of the plurality of landmarks to the defined landmark configurations associated with the plurality of defined gestures, a defined gesture included in the plurality of defined gestures, and directing the computer system to enter a user input that is associated with the defined gesture.

The present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle by comparing current image feature data to previously classified image features.

The disclosure relates to a method and a system for hand pose recognition, a device and a storage medium are disclosed in embodiments of the disclosure. The method includes: capturing a RGB image of a hand from a RGB camera and capturing a depth image of the hand from an active depth camera, so as to obtain a hand pose data set according to the RGB image and the depth image; processing the hand pose data set to obtain a 3D joint position, and taking the 3D joint position as a data set for training a software model; extracting the RGB image by a feature extractor based on a depth neural network to obtain a feature map of a hand pose; and processing the feature map according to an attention mechanism to obtain a global feature map of the hand pose.