Abstract: A face mask detection system for detecting whether a user is wearing a mask at any given time using simple image recognition, configured to obtain an image of the user with the face mask, validate the image of the user wearing the face mask by an administrator (different from the user) in order to confirm validity of the image, and as a subsequent step use the validated image of the user with the face mask as a model image for benchmarking purposes. The model image is preferably a head profile of the user wearing a face mask. A face mask investigation unit compares the model image to an investigation image captured by an imaging system during an investigation process to determine whether the user in the investigation image is wearing the face mask based on whether an exact match is found between the model image and the investigation image.

Abstract: This disclosure describes techniques for authenticating a user. For instance, a system may initially generate and then store authentication data for later authenticating the user. The authentication data may include biometrics data (e.g., facial recognition data), credentials data (e.g., a username, password, etc.), and/or environmental data (e.g., object(s) located within an environment). Later, the system may receive image data generated by a user device of the user. In some examples, the system may then analyze the image data using one or more facial recognition techniques in order to identify the user. The system may then use the authentication data, such as the environmental data, to determine that the image data further represents the object(s). Based on determining that the image data further represents the object(s), the system may then verify the user for access to a resource.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that use generative adversarial models to increase the quality of sensor data generated by a first environmental sensor to resemble the quality of sensor data generated by another sensor having a higher quality than the first environmental sensor. A set of first and second training data generated by a first environmental sensor having a first quality and a second sensor having a target quality, respectively, is received. A generative adversarial mode is trained, using the set of first training data and the set of second training data, to modify sensor data from the first environmental sensor by reducing a difference in quality between the sensor data generated by the first environmental sensor and sensor data generated by the target environmental sensor.

Abstract: Examples described herein provide a method that includes performing cluster matching with one or more cluster sizes for each of a plurality of points of a measurement point cloud. The method further includes determining, based on results of the multi-radii cluster matching, whether an object is displaced or whether the object includes a defect.

Abstract: Systems, and method and computer readable media that store instructions for features matching based tracker.

Abstract: An artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for interactive image segmentation. An example apparatus includes an inception controller to execute an inception sublayer of a convolutional neural network (CNN) including two or more inception-atrous-collation (IAC) layers, the inception sublayer including two or more convolutions including respective kernels of varying sizes to generate multi-scale inception features, the inception sublayer to receive one or more context features indicative of user input; an atrous controller to execute an atrous sublayer of the CNN, the atrous sublayer including two or more atrous convolutions including respective kernels of varying sizes to generate multi-scale atrous features; and a collation controller to execute a collation sublayer of the CNN to collate the multi-scale inception features, the multi-scale atrous features, and eidetic memory features.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive image data from a camera, generate a depth map from the image data, detect an object in the image data, apply a bounding box circumscribing the object to the depth map, mask the depth map by setting depth values for pixels in the bounding box in the depth map to a depth value of a closest pixel in the bounding box, and determine a distance to the object based on the masked depth map. The closest pixel is closest to the camera of the pixels in the bounding box.

Abstract: An apparatus for remote processing of raw image data receives the raw image data from a camera, such as a security camera. The apparatus includes a detection module to detect portions of the image data that contain possible regions of interest. Information indicating the portions that contain the possible regions of interest is then used during a compression process so that the portions that contain the possible regions of interest are compressed using one or more compression algorithms to facilitate further analysis and the remainder are treated differently. The compressed image data is then transmitted to a central system for decompression and further analysis. In some cases, the detection system may detect possible regions of interest which appear to be faces, but without performing full facial recognition. These parts of the image data are then compressed in such a way as to maintain as much facial detail as possible, so as to facilitate the facial recognition when it is carried out at the central server.

Abstract: A processing device includes a memory configured to store executable instructions, and processing circuitry configured to execute the executable instructions stored in the memory to thereby realize: a first acquisition unit configured to acquire timing of blink motion performed by a dialogue device, a second acquisition unit configured to acquire blink timing by user performing a dialogue with the dialogue device, and a processing unit configured to perform processing according to difference between the timing of blink motion performed by the dialogue device and the blink timing by the user.

Abstract: Various techniques employ digital fingerprints, alone or in conjunction with other data related to a physical object, to characterize the physical object in a way allows an identity of the physical object to be confirmed or authenticated within a threshold of confidence when the physical object is encountered at a subsequently after a digital fingerprint thereof was previously ingested, without capture, retention and/or reference to certain types or categories of characterizing information which may be of a sensitive nature and/or subject to misuse or abuse. Such digital fingerprints may include feature vectors and non-positional characterization values, while advantageously omitting positional information that would otherwise allow recreation of a recognizable image of the physical object or an image from which one or more immutable characteristics or traits (e.g.

Abstract: A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.

Abstract: There is described a deep learning supervised regression based model including methods and systems for facial attribute prediction and use thereof. An example of use is an augmented and/or virtual reality interface to provide a modified image responsive to facial attribute predictions determined from the image. Facial effects matching facial attributes are selected to be applied in the interface.

Abstract: This disclosure describes techniques to aggregate sensor data. The techniques perform operations comprising: receiving, from a first sensor, a first profile representing a first set of movement attributes detected by the first sensor in an area at a given point in time; receiving, from a second sensor, a second profile representing a second set of movement attributes detected by the second sensor in the area at the given point in time; computing a similarity measure between the first and second sets of movement attributes of the first and second profiles; determining that the similarity measure exceeds a threshold value; and in response to determining that the similarity measure exceeds the threshold value, associating the first and second profiles with a same first object that is in the area at the given point in time.

Abstract: A method for controlling an ego agent includes capturing a two-dimensional (2D) image of an environment adjacent to the ego agent. The method also includes generating a semantically segmented image of the environment based on the 2D image. The method further includes generating a depth map of the environment based on the semantically segmented image. The method additionally includes generating a three-dimensional (3D) estimate of the environment based on the depth map. The method also includes controlling an action of the ego agent based on the identified location.

Abstract: The disclosure herein describes systems and methods for object data storage. In some examples, the method includes generating a profile for an object in a directory, the profile including a first feature vector corresponding to the object and a global unique identifier (GUID) corresponding to the first feature vector in the profile; generating a search scope, the search scope including at least the GUID corresponding to the profile; generating a second feature vector from a live image scan; matching the generated second feature vector from the live image scan to the first feature vector using the generated search scope; identifying the GUID corresponding to the first feature vector that matches the second feature vector; and outputting information corresponding to the object of the profile identified by the GUID corresponding to the first feature vector.

Abstract: Methods, systems and computer readable medium for liveness detection authentication of a facial image are provided. The method includes acquiring a first image of the facial image illuminated with a first color, acquiring a second image of the facial image illuminated with a second color, and determining if the facial image is consistent with a three-dimensional (3D) structure in response to a combination of the first and second images. The method further includes authenticating the facial image if the facial image is consistent with a 3D structure and the facial image matches a face of user to be authenticated.

Abstract: An image processing method executed by a computer, the method includes detecting a plurality of feature points of a face from an input image, referring to importance information that indicates an importance of a region within an image in a process of detecting a predetermined facial motion from the image, selecting, from the plurality of feature points detected by the detecting, one or more points that correspond to an image region including an importance indicated by the importance information equal to or smaller than a first threshold value, correcting the input image by using the one or more points selected by the selecting, to generate a corrected image; and determining whether or not the predetermined facial motion is occurring in the input image, based on an output obtained by inputting the corrected image to a recognition model.

Abstract: A method of host-directed illumination for verifying the validity of biometric data of a user is provided that includes capturing biometric data from a user with an authentication device during authentication and directing illumination of the biometric data from a host authentication system during the capturing operation. Moreover, the method includes comparing illumination characteristics of the captured biometric data against illumination characteristics expected to result from the directing operation, and determining that the user is a live user when the illumination characteristics of the captured biometric data match the illumination characteristics expected to result from the directing operation.

Abstract: Disclosed is a facial recognition system/method, utilizing modules to perform the following routine: extracting a feature descriptor from a detected feature point of a detected face in an input image frame; and matching the extracted feature descriptor with at least one of a pre-stored facial image that is index-mapped, comprising at least a first and second round of matching, wherein the second round of matching only selects the index-mapped facial images that matched above a pre-defined threshold from the first round of matching. Optionally, the above described steps may be coupled to a Point-of-Recognition (POR) provisioning, enabling an on-demand gate-keeping and/or payment processing for an end-user at an event/venue entry or point-of-sale.