The present disclosure provides a method for performing identity recognition on a to-be-recognized object, an electronic device, and a non-transitory computer-readable storage medium. The method includes: acquiring, by the infrared camera, and in response to an infrared camera turn-on condition being met, a first image of a to-be-recognized target, and performing target detection on the first image, the to-be-recognized target being a finger and/or a palm; acquiring, by the visible light camera, and in response to a visible light camera turn-on condition being met, a second image of the to-be-recognized target, and performing identifier code recognition on the second image; performing, in response to the to-be-recognized target being detected from the first image, identity recognition based on a third image, and determining an identity recognition result of the to-be-recognized object, the third image being at least one image among the first image in which the to-be-recognized target is detected.

A method, system, apparatus, and/or device for moving or scrolling a virtual object in a virtual or augmented reality environment. The method, system, apparatus, and/or device may include: detecting, by a first sensor, a first gesture associated with selecting a first virtual object in an augmented reality environment displayed by a head-mounted display; displaying, by the head-mounted display, a first indicator indicating a selection of the first virtual object by a user; detecting, using the first sensor or a second sensor, a first movement of the head-mounted display associated with a first movement command; and in response to detecting the first movement of the head-mounted display, executing the first movement command, where the first movement command is a scrolling function to scroll text or a graphical object of the first virtual object or a movement function to move the text or the graphical object of the first virtual object.

A system of enhancing biometric analysis matching utilizes an image sensor, such as a digital camera, to capture an image of a face of a person. The system may perform image enhancement, such as edge and contrast enhancement, prior to performing face matching. The enhancement may be localized to a given image region based on determined region illumination. The system may perform image processing and analysis comprising face detection, alignment, feature extraction, and recognition. A biometric recognition confidence indicator may be generated using the results of the image enhancement and analysis. At least partly in response to the biometric recognition confidence indicator falling below a threshold enhancing recognition confidence using an image of visual indicia captured using the image sensor.

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.

There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

A biometric identification system uses inputs acquired using different modalities. A model having an intersection branch and an XOR branch is trained to determine an embedding using features present in all modalities (an intersection of modalities), and features that are distinctive to each modality (an XOR of that modality relative to the other modality(s)). During training, a first loss function is used to determine a first loss value with respect to the branches. Probability distributions are determined for the output from the branches, corresponding to the intersection and XORs of each modality. A second loss function uses these probability distributions to determine a second loss value. A total loss function for training the model may be a sum of the first loss and the second loss. Once trained, the model may process query inputs to determine embedding data for comparison with embedding data of a previously enrolled user.

The present teaching relates to method, system, medium, and implementations for detecting liveness. An input image is received with visual information claimed to represent a palm of a person. One or more variations of the input image are generated based on one or more models specifying conditions to generate the variations. The liveness of the visual information is detected based on the input image and the one or more variations thereof.

There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

A method, system, apparatus, and/or device for detecting pinch gestures in an augmented reality environment. The method, system, apparatus, and/or device may include: a wearable display, a sensor, and a processing device. The wearable display may be configured to attach to a head of a user and display an augmented reality environment to the user. The sensor may be configured to detect a position of a first digit of a hand of the user and detect a position of a second digit of the hand of the user. The processing device may be configured to: identify a first fingertip of the first digit; identify a second fingertip of the second digit; determine that the first fingertip and the second fingertip are in an open pinch position at a first point in time; and display a cursor at a midpoint between the first fingertip and the second fingertip.

A scanner acquires a set of images of a hand of a user to facilitate identification. These images may vary, due to changes in relative position, pose, lighting, obscuring objects such as a sleeve, and so forth. A first neural network determines output data comprising a spatial mask and a feature map for individual images in the set. The output data for two or more images is combined to provide aggregate data that is representative of the two or more images. The aggregate data may then be processed using a second neural network, such as convolutional neural network, to determine an embedding vector. The embedding vector may be stored and associated with a user account. At a later time, images acquired from the scanner may be processed to produce an embedding vector that is compared to the stored embedding vector to identify a user at the scanner.