A collation system of the present invention includes an imaging means for acquiring a captured image of a pre-passage side area with respect to a gate, a collation means for performing a collation process between a previously registered target and a target in the captured image, and a determination means for determining propriety of passage of the target with respect to the gate, on the basis of a result of the collation process. The collation means initiates the collation process on the basis of a condition, set to each area of the captured image, for the target located in the area.

Computer implemented methods and devices for determining dimensions or distances of head features are provided. The method includes identifying a plurality of features in an image of a head of a person. A real dimension of at least one target feature of the plurality of features or a real distance between at least one target feature of the plurality features and a camera device used for capturing the image is estimated based on probability distributions for real dimensions of at least one feature of the plurality of features and a pixel dimension of the at least one feature of the plurality of features.

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

A system and method of detecting display fit measurements and/or ophthalmic measurements for a head mounted wearable computing device including a display device is provided. The system and method may include capturing image data including a face of a user to be fitted for the head mounted wearable computing device. A three-dimensional head pose and gaze measurements may be extracted and a three-dimensional model may be developed from the captured image data. The system may detect display fit measurements and/or ophthalmic fit measurements from the three-dimensional model, and may provide one or more head mounted wearable computing devices that meet the display fit and/or ophthalmic fit requirements.

A tooth brushing game includes starting counting a time that defines one play of a toothbrush game in response to starting the toothbrush game; capturing an image of a user; setting an area around a mouth by detecting a face area of the user from the captured image and detecting a facial part from the detected face area; detecting movement of an object including a toothbrush and a hand in the area around the mouth; controlling the toothbrush game based on the detected movement of the object; and stopping the counting of the time when the area around the mouth is not set but not stopping the counting when the movement of the object is not detected.

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.

Methods, apparatus and systems for recognizing sign language movements using multiple input and output modalities. One example method includes capturing a movement associated with the sign language using a set of visual sensing devices, the set of visual sensing devices comprising multiple apertures oriented with respect to the subject to receive optical signals corresponding to the movement from multiple angles, generating digital information corresponding to the movement based on the optical signals from the multiple angles, collecting depth information corresponding to the movement in one or more planes perpendicular to an image plane captured by the set of visual sensing devices, producing a reduced set of digital information by removing at least some of the digital information based on the depth information, generating a composite digital representation by aligning at least a portion of the reduced set of digital information, and recognizing the movement based on the composite digital representation.

A digital makeup artist system includes a mobile device, a database system storing cosmetic routine information, common makeup looks, cosmetic products for skin types and ethnicity, and user look preferences of a user. The mobile device includes a user interface for interacting with a digital makeup artist. The digital makeup artist performs an interactive dialog with the user in order to capture needs of the user, including types of makeup look, indoor or outdoor look, skin condition, facial problem areas, favorite facial features. The computation circuitry analyzes the user's face image to identify face parts, analyzes the face image to determine facial characteristics, and generates image frames to be displayed in synchronization with the interaction with the digital makeup artist based on the analyzed face image, needs of the user, the stored cosmetic routine information, common makeup looks, cosmetic products for skin types and ethnicity, and the user look preferences.

A neural network image processing apparatus arranged to acquire images from an image sensor and to: identify a ROI containing a face region in an image; determine at plurality of facial landmarks in the face region; use the facial landmarks to transform the face region within the ROI into a face region having a given pose; and use transformed landmarks within the transformed face region to identify a pair of eye regions within the transformed face region. Each identified eye region is fed to a respective first and second convolutional neural network, each network configured to produce a respective feature vector. Each feature vector is fed to respective eyelid opening level neural networks to obtain respective measures of eyelid opening for each eye region. The feature vectors are combined and to a gaze angle neural network to generate gaze yaw and pitch values substantially simultaneously with the eyelid opening values.

Various embodiments are directed to performing identity verification using biometrics and open data, such as publicly available data on the Internet. A person may provide various types of information about the person, including a name and an image of the person. An Internet search may be performed on the provided name and one or more publicly-available images corresponding to that name may be acquired. Biometric analyses may be performed on both the image provided by the person and the acquired one or more images to determine whether any of them match the person. Metadata may be extracted from a matching image. Moreover, data relating to the person may be acquired from the source of the matched image. The metadata and the data from the source may be compared with the information provided by the person to validate the identity of the person.