State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

A gaze point calculation apparatus includes: a first cache register, a multiplexer, an arithmetic unit assembly, and a state machine. The first cache register is configured to receive and store first coordinates and a plurality of calibration parameters required when second required are obtained through calculation according to the first coordinates. The state machine is configured to control the multiplexer to select each time at least one value from the first cache register and transmit same to the arithmetic unit assembly. The arithmetic unit assembly is configured to perform a preset operation on the at least one value received each time until the second coordinates are obtained, and output the second coordinates under control of the state machine.

Biometrics are increasingly used to provide authentication and/or verification of a user in many security and financial applications for example. However, “spoof attacks” through presentation of biometric artefacts that are “false” allow attackers to fool these biometric verification systems. Accordingly, it would be beneficial to further differentiate the acquired biometric characteristics into feature spaces relating to live and non-living biometrics to prevent non-living biometric credentials triggering biometric verification. The inventors have established a variety of “liveness” detection methodologies which can block either low complexity spoofs or more advanced spoofs. Such techniques may provide for monitoring of responses to challenges discretely or in combination with additional aspects such as the timing of user's responses, depth detection within acquired images, comparison of other images from other cameras with database data etc.

A display device includes a directional illuminator providing a light beam, a display panel downstream of a directional illuminator, for receiving and spatially modulating the light beam, and a beam redirecting module downstream of the display panel, for variably redirecting the spatially modulated light beam. Steering the illuminating light by the beam redirecting module enables one to steer the exit pupil of the display device to match the user's eye location(s).

An eye tracking system for tracking one or more of a user's eyes includes an closed-eye detector operable to detect when a user has closed one or more of their eyes, an eye tracker operable to detect an eye orientation in dependence upon a measured deformation of an eyelid corresponding to the an eye that has been detected to be shut, and an image renderer operable to render a foveated image for display in response to the detected eye orientation.

Disclosed herein are systems and methods for presenting an audio signal associated with presentation of a virtual object colliding with a surface. The virtual object and the surface may be associated with a mixed reality environment. Generation of the audio signal may be based on at least one of an audio stream from a microphone and a video stream form a sensor. In some embodiments, the collision between the virtual object and the surface is associated with a footstep on the surface.

An electronic device includes a first camera module; a display for displaying an augmented reality content; a processor operatively connected to the first camera module and the display; and a memory operatively connected to the at least one processor and storing instructions and dominant eye correction information. The processor performs a dominant eye determination operation of a user of the electronic device identifying a first position of a first object during the dominant eye determination operation; identifies a second position of the first object by using the first camera module; recognizes, as a selected point, a point moved, by a first correction value determined on the basis of the dominant eye correction information, from a first reference point displayed on the augmented reality content during the dominant determination operation; and selects a second object on the augmented reality content corresponding to the selected point.

A head-mounted wearable device includes a frame mountable on a head of a user; an infrared imaging device arranged to image a face of the user when the frame is mounted on the head of the user; and a computing system configured to perform operations including causing the infrared imaging device to capture an image of the face of the user using infrared light received at the infrared camera and initiating a biometric authentication process based on the image. The head-mounted wearable device may include a visible-light imaging device to image the face of the user with the computing system configured to perform operations including causing the visible-light imaging device to capture a second image of the face of the user using visible light received at the visible-light imaging device, with the biometric authentication process being based in part on the second image.

An example apparatus for adjusting eye gaze in images one or more processors to execute instructions to bidirectionally train a neural network; access a target angle and an input image, the input image including an eye in a first position; generate a vector field with the neural network; and generate a gaze-adjusted image based on the vector field, the gaze-adjusted image including the eye in a second position.

A system and a method of calculating coordinates of a pupil center point are provided. The system for acquiring the coordinates of the pupil center point includes a first camera, a second camera, a storage and a processor. The first camera is configured to capture a first image including a face and output the first image to the processor, the second camera is configured to capture a second image including a pupil and output the second image to the processor, a resolution of the first camera is smaller than a resolution of the second camera, and the storage is configured to store processing data, and the processor is configured to: acquire the first image and the second image; extract a first eye region corresponding to an eye from the first image; convert the first eye region into the second image, to acquire a second eye region corresponding to the eye in the second image; and detect a pupil in the second eye region and acquire the coordinates of the pupil center point.