Techniques are described for passive three-dimensional (3D) object authentication based on image sizing, such as for biometric facial recognition. For example, during a registration routine, an imaging system captures images of a registering user's face at multiple distances. The images can be processed to extract registration dimensions, including individual deterministic structural dimensions, dimensional relationships that are static over changes in imaging distance, and dimensional relationships that changes predictably over changes in imaging distance. During an authentication routine, the imaging system again captures authentication images of an authenticating user's face (purportedly the previously registered user) at some authentication imaging distance and processes the images to extract authentication dimensions. Expected and actual dimensional quantities are computed from the authentication and registration dimensions and are compared to determine whether the authenticating user's face appears to be authorized as previously registered and/or is a spoof.

A system and a method for performing 2D on-screen user gaze estimation using an input facial image of a user captured using a camera associated to a processing device having a display. The method and system allow automated user calibration through automatic recording of calibration samples each including a calibration facial image of the user and an interaction point corresponding to a point on the display where an occurrence of a user interaction was detected when the corresponding calibration image was captured. The system and method also optimize user-specific parameters using the calibration samples by iteratively minimizing a total difference between the interaction points of a plurality of the calibration samples and corresponding 2D gaze estimation results and convert an estimated 3D gaze direction into a 2D gaze estimation result corresponding to a point on the display, by applying the users-specific parameters.

Provided are a method and apparatus for turning on a screen, a mobile terminal and a storage medium. The method comprises that: when a change in a moving state of a mobile terminal meets a preset unlocking condition, a structured light image sensor is activated for imaging; a depth map obtained by the imaging of the structured light image sensor is acquired; a face depth model is constructed according to the depth map; a position of pupils is identified from the face depth model; and when the position of the pupils is within a specified region of eyes, the screen of the mobile terminal is controlled to turn on.

A dental device tracking method including acquiring, using an imager of a dental device, at least a first image which includes an image of at least one user body portion outside of a user's oral cavity; identifying the at least one user body portion in the first image; and determining, using the at least the first image, a position of the dental device with respect to the at least one user body portion.

An image processing method includes receiving an image frame, detecting a face region of a user in the image frame, aligning a plurality of preset feature points in a plurality of feature portions included in the face region, performing a first check on a result of the aligning based on a first region corresponding to a combination of the feature portions, performing a second check on the result of the aligning based on a second region corresponding to an individual feature portion of the feature portions, redetecting a face region based on a determination of a failure in passing at least one of the first check or the second check, and outputting information on the face region based on a determination of a success in passing the first check and the second check.

Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D/3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service).

A vehicle is provided and includes a gesture detector that detects a gesture of a driver to designate a manipulation target object and a voice recognizer that recognizes a voice command generated by the driver to operate the designated manipulation target object. A controller is configured to transmit a control signal corresponding to the voice command to the manipulation target object, and to operate the manipulation target object to perform an operation corresponding to the voice command.

An audio transducer device includes an audio transducer, a controller and a direction adjusting mechanism. The audio transducer has a sound receiving surface formed with multiple sound collecting holes, and multiple microphones corresponding in position to the sound collecting holes. The controller is detachably mounted to an electronic device, and controls the microphones to cooperatively perform directional sound reception to obtain audio data. The direction adjusting mechanism interconnects an audio transducer shell and the controller such that the sound receiving surface can be rotated to a position where a normal direction thereof and an image capturing direction of the electronic device forming a desired angle therebetween.

A computer-implemented method includes receiving a two-dimensional (2-D) side view face image of a person, identifying a bounded portion or area of the 2-D side view face image of the person as an ear region-of-interest (ROI) area showing at least a portion of an ear of the person, and processing the identified ear ROI area of the 2-D side view face image, pixel-by-pixel, through a trained fully convolutional neural network model (FCNN model) to predict a 2-D ear saddle point (ESP) location for the ear shown in the ear ROI area. The FCNN model has an image segmentation architecture.

A computer-implemented method for correcting data to match user identity may include (i) receiving user input specifying an aspect of physical presentation of the user that does not match an authentic identity of the user, where the authentic identity of the user includes a realistic version of the user that reflects an internal self-image of the user, (ii) capturing, via a sensor, data of the user that includes the aspect of the physical presentation of the user, (iii) correcting the captured data of the user to portray a corrected version of the aspect that matches the authentic identity of the user, and (iv) storing the corrected data of the user that matches the authentic identity of the user instead of uncorrected data of the user that includes the aspect that does not match the authentic identity of the user. Various other methods, systems, and computer-readable media are also disclosed.