Abstract: A screen recording method includes displaying an interface including a plurality of split screen areas; determining a target object in response to an initiation of a screen recording function, the determined target object including at least one of the plurality of split screen areas; based on a determination that the target object includes a plurality of audio sources that are playing at the same time, receiving a selection of at least one audio source among the plurality of audio sources; and recording screen display content and the at least one audio source of the target object to generate a screen recording file.

Abstract: An information processing apparatus (100) includes an acquisition unit (122) that acquires a first image from which person region feature information regarding a region including other than a face of a retrieval target person is extracted, a second image in which a collation result with the person region feature information indicates a match, and a facial region is detected, and result information indicating a collation result between face information stored in a storage unit and face information extracted from the facial region, and a display processing unit (130) that displays at least two of the first image, the second image, and the result information on an identical screen.

Abstract: An information processing apparatus (100) includes an acquisition unit (122) that acquires a first image from which person region feature information regarding a region including other than a face of a retrieval target person is extracted, a second image in which a collation result with the person region feature information indicates a match, and a facial region is detected, and result information indicating a collation result between face information stored in a storage unit and face information extracted from the facial region, and a display processing unit (130) that displays at least two of the first image, the second image, and the result information on an identical screen.

Abstract: A system for identifying users for entrance to an area comprising a point of entry into a pre-defined area. The system comprises a first computing device having a microprocessor, a memory and a screen disposed adjacent to the point of entry. There is also at least one second computing device having at least one microprocessor and at least one memory, said at least one second computing device being disposed remote from the first computing device. There is at least one third computing device having at least one screen and at least one microprocessor wherein the third computing device is in communication with the second computing device wherein the second computing device is configured to send at least one image to the first computing device and to the third computing device wherein when an image at the point of entry matches an image on the third computing device the port of entry will allow the user holding the third computing device into the point of entry.

Abstract: 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.

Abstract: An object re-identifier. For each of a plurality of frames of a video, a quality of the frame is assessed and a confidence that a previously-recognized object is present in the frame is determined. The determined confidence for the frame is weighted based on the assessed quality of the frame such that frames with higher relative quality are weighted more heavily than frames with lower relative quality. An overall confidence that the previously-recognized object is present in the video is assessed based on the weighted determined confidences.

Abstract: An operation of a facial recognition authentication process may fail to authenticate a user even if the user is an authorized user of the device. In such cases, the facial recognition authentication process may automatically re-initiate to provide another attempt to authenticate the user using additional captured images. For the new attempt (e.g., the retry) to authenticate the user, one or more criteria for the images used in the facial recognition authentication process may be adjusted. For example, criteria for distance between the camera and the user's face and/or occlusion of the user's face in the images may be adjusted before the new attempt to authenticate the user. Adjustment of these criteria may increase the likelihood that the authorized user will be successfully authenticated in the new attempt.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating behavioral attribute data structures.

Abstract: A driver monitor includes a processor configured to detect the orientation and the position of the face of a driver of a vehicle from each of face images obtained by a camera for capturing the driver, update a reference direction of orientations or a reference position of the driver's face, based on orientations or positions of the driver's face detected from face images obtained in a preceding predetermined period, and detect putting a wearable object on or taking a wearable object off the driver's face in each face image. Upon detection of putting on or taking off the wearable object, the processor updates the reference direction or the reference position, based on only orientations or positions of the driver's face detected from face images obtained after the detection.

Abstract: Systems and methods for enrolling and authenticating a user in an authentication system via a user's camera of camera equipped mobile device include capturing and storing enrollment biometric information from at least one first image of the user taken via the camera of the mobile device, capturing authentication biometric information from at least one second image of the user, capturing, during imaging of the at least one second image, path parameters via at least one movement detecting sensor indicating an authentication movement of the mobile device, comparing the authentication biometric information to the stored enrollment biometric information, and comparing the authentication movement of the mobile device to an expected movement of the mobile device to determine whether the authentication movement sufficiently corresponds to the expected movement.

Abstract: Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for user identification using Artificial Intelligence, Machine Learning, and data analytics. In one embodiment, a verification system and method is introduced that can provide user authentication using parallel modeling for face identification. The verification system used includes a face identification module for use in the identification and verification using parallel processing of a received image with a claimed identity. The parallel processing includes an ensemble of machine learning models processed in parallel for optimal performance.

Abstract: An authentication method and apparatus using a transformation model are disclosed. The authentication method includes generating, at a first apparatus, a first enrolled feature based on a first feature extractor, obtaining a second enrolled feature to which the first enrolled feature is transformed, determining an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor, and performing an authentication based on the second enrolled feature and the input feature.

Abstract: An unattended approach can increase the reproducibility and safety of the treatment as the chance of over/under treating of a certain area is significantly decreased. On the other hand, unattended treatment of uneven or rugged areas can be challenging in terms of maintaining proper distance or contact with the treated tissue, mostly on areas which tend to differ from patient to patient (e.g. facial area). Delivering energy via a system of active elements embedded in a flexible pad adhesively attached to the skin offers a possible solution. The unattended approach may include delivering of multiple energies to enhance a visual appearance.

Abstract: A living body detection method and device are disclosed. Wherein the method comprises the following steps: extracting valid depth data of a target detection object from depth map data containing the target detection object; generating a depth difference histogram based on the valid depth data; and inputting the depth difference histogram into a pre-trained machine learning classifier to obtain a determination result of whether the target detection object is a living body. By adopting this method, the detection accuracy can be improved.

Abstract: The system receives position information in both an internal coordinate frame and an external coordinate frame. The system uses a comparison of position information in these frames to determine orientation information. The system determines one or more orientation hypotheses, and analyzes the position information based on these hypotheses. The system may include on-board accelerometers, gyroscopes, or both that provide the measurements in the internal coordinate frame. These measurements may be integrated or otherwise processed to determine position, velocity, or both. Measurements in the external frame are provided by GPS sensors or other positioning systems. Position information is transformed to a common coordinate frame, and an error metric is determined. Based on the error metric, the system estimates a likelihood metric for each hypothesis, and determines a resulting hypothesis based on the maximum likelihood or a combination of likelihoods.

Abstract: A video gateway device at a worksite (or other location) is coupled to multiple cameras on a network, such as multiple cameras at a business site. The gateway device determines video stream capabilities of each camera and, based on the capabilities, determines whether any additional video steams need to be created for particular cameras. For example, for cameras that provide only a high-resolution video stream, the gateway will generate (e.g., via downsampling) a standard-resolution video stream for artificial intelligence (“AI”) analysis and a low-resolution video stream for viewing by a user. However, for cameras that provide both a high and standard-resolution video stream, the gateway device uses the camera-provided standard-resolution video stream for AI analysis, rather than generating a new video stream on the gateway device.

Abstract: An illustrative resolution is generated using a computer for a reported operational issue while using a computer system. A learning model is developed using the computer to facilitate resolving an operational error of a computer system. The learning model can include generating an error report, based on tracking user inputs into a computer system and a plurality of state transitions of the computer system, respectively, when the operational error is not resolved by the user inputs. An error screenshot for each operational step associated with the user inputs is generated. A state transition is cross referenced to a corresponding operational step, and to a best practice in a user's manual, respectively. A deviation of an operational step from a best practice in the user's manual, respectively, is generated. An error screenshot is marked illustratively to show the deviation from the best practice in comparison to the operational step.

Abstract: An electronic device and method for texture map generation are disclosed. A set of color images from a set of viewpoints and depth information of a subject's face are acquired. A 3D model is generated based on a color image and the depth information. A set of viewpoint-specific projections of the 3D model are generated along the set of viewpoints. The 3D model is refined based on minimization of difference between each viewpoint-specific projection and a corresponding color image. A set of texture maps corresponding to the set of viewpoints is generated, based on the refined 3D model, and set of color images. A dynamic programming workflow is executed to determine seams along which respective portions of the set of texture maps are to be stitched and the respective portions are stitched along the seams to generate a final texture map for the refined 3D model of the subject's face.

Abstract: An image matching method includes extracting, from a first image of an object, a landmark patch including a landmark point of the object; extracting, from a second image of the object, a target patch corresponding to the landmark patch; and determining a target point in the second image corresponding to the landmark point based on a matching between the landmark patch and the target patch.

Abstract: Systems and methods are provided for control of a personal computing device based on user face detection and recognition techniques.

Abstract: Training a robust machine learning model by mapping an input data set to a first feature space, applying a transformation to the first feature space, yielding a second feature space, and training a dense model using the first feature space, and the second feature space.

Abstract: Systems and methods for demographic determination using image recognition. The method includes analyzing an image with a pre-trained lightweight neural network model, where the lightweight neural network model generates a confidence value, and comparing the confidence value to a threshold value to determine if the pre-trained lightweight neural network model is sufficiently accurate. The method further includes analyzing the image with a pre-trained heavyweight neural network model for the confidence value below the threshold value, wherein the pre-trained heavyweight neural network model has above about one million trainable parameters and the pre-trained lightweight neural network model has a number of trainable parameters below one tenth the heavyweight model, and displaying demographic data to a user on a user interface, wherein the user modifies store inventory based on the demographic data.

Abstract: In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device.

Abstract: An iris or other object detection method and apparatus are disclosed. In one embodiment, the method comprises sending image data to a display of a device that is captured with a first camera of the device with an indication to guide a user to position a body part of the user with respect to the display while the image data is being sent to the display, providing feedback to the user to indicate to the user that the body part is in position so that an image of the body part can be captured by a second camera of the device, capturing an image of the body part with the second camera, and performing recognition on the body part using the image.

Abstract: Described are methods, systems, and computer-program product embodiments for selecting a face image based on a name. In some embodiments, a method includes receiving the name. Based on the name, a name vector is selected from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, where each name vector includes representations associated with a plurality of words associated with each name. A plurality of face vectors corresponding to a plurality of face images is received. A face vector is selected from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, where for each name vector, a similarity score is calculated based on the name vector and each face vector. The face image is output based on the selected face vector.

Abstract: The present application relates to the field of computer software technologies, and in particular, to a face spoofing attack detection method and system. The suggested method comprises capturing an image; determining a context of the captured image; highlighting a part of the captured image to generate an image with the highlighted part, wherein the highlighted part is a potential spoofing region determined based on the context of the captured image; combining the captured image with the image with the highlighted part to generate a summarized image; performing face spoofing attack detection in the summarized image, wherein only the highlighted part has priority in the face spoofing attack detection, generating a general spoofing score as a result of the face spoofing attack detection; comparing the general spoofing score result with a threshold for face liveness detection.

Abstract: A system including an avatar generation engine (AGE) (607) and a method for automatically generating an avatar with pronounced features are provided. The AGE (607) extracts a primary image of a primary component, for example, a face component, and a secondary image of a secondary component, for example, a hair component, from an input image of a target object. The AGE (607) normalizes and processes the primary image for extracting a feature image corresponding to a feature indicating a distinct characteristic of the target object. The AGE (607) processes the extracted feature image for graphically pronouncing the features. The AGE (607) generates a primary canvas including a predicted tone of the primary component. The AGE (607) generates an avatar with pronounced features by merging a primary graphical image generated by merging the primary canvas with the graphically pronounced features, with a secondary graphical image of the secondary component.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and the processor configured to: acquire a plurality of sample videos; identify a position and time at which an attribute appears in each of the plurality of sample videos, the attribute being output by each of one or more pre-trained models to which each of the plurality of sample videos is input; cluster attribute labels based on the position and time of the attribute for each of the plurality of sample videos; and generate a rule by combining attribute labels included in a cluster having a highest frequency of appearance among cluster groups obtained for all of the plurality of sample videos.

Abstract: Techniques are disclosed for managing image capture and processing in a multi-camera imaging system. In such a system, a pair of cameras each may output a sequence of frames representing captured image data. The cameras' output may be synchronized to each other to cause synchronism in the image capture operations of the cameras. The system may assess image quality of frames output from the cameras and, based on the image quality, designate a pair of the frames to serve as a “reference frame pair.” Thus, one frame from the first camera and a paired frame from the second camera will be designated as the reference frame pair. The system may adjust each reference frame in the pair using other frames from their respective cameras. The reference frames also may be processed by other operations within the system, such as image fusion.

Abstract: Systems and methods for authenticating a user in an authentication system using a computing device configured to capture authentication biometric identity information. The authentication biometric identify information captured during an authentication session. The authentication biometric identify information may comprise or be derived from one or more images of the user being authenticated. The authentication biometric identify information is compared to root identify biometric information. The root identify biometric information is captured from a trusted source, such as trusted devices located at trusted locations, such as a government entity, financial institution, or business. Identity verification may occur by comparing the trusted root identify biometric information to the biometric identify information captured during an authentication session. Liveness determination may also occur to verify the user is a live person.

Abstract: A computer-implemented method for creating a photo product design with minimal user inputs includes generating a first portion of a photo product design by a product design module in response to user input, determining one or more layout parameters from the user input to the first portion of the photo product design by a user design learning module, automatically creating a second portion of the photo product design by an intelligent product creation engine at least in part in response to the one or more layout parameters, and automatically making a physical photo product based on at least the first portion and the second portion of the photo product design.

Abstract: A driver monitor includes a processor configured to detect the orientation and the position of the face of a driver of a vehicle from each of face images obtained by a camera for capturing the driver, update a reference direction of orientations or a reference position of the driver's face, based on orientations or positions of the driver's face detected from face images obtained in a preceding predetermined period, and detect putting a wearable object on or taking a wearable object off the driver's face in each face image. Upon detection of putting on or taking off the wearable object, the processor updates the reference direction or the reference position, based on only orientations or positions of the driver's face detected from face images obtained after the detection.

Abstract: Systems and methods for face recognition are provided. The systems may perform the methods to obtain a neural network comprising a first sub-neural network and a second sub-neural network; generate a plurality of preliminary feature vectors based on an image associated with a human face, the plurality of preliminary feature vectors comprising a color-based feature vector; obtain at least one input feature vector based on the plurality of preliminary feature vectors; generate a deep feature vector based on the at least one input feature vector using the first sub-neural network; and recognize the human face based on the deep feature vector.

Abstract: A method for displaying bullet screens can include: acquiring detection boxes by detecting human-body parts in a plurality of image frames in a target video; and determining a masked region of each of the image frames based on detection boxes in the each of the image frames and a ratio of the detection boxes in the each of the image frames in an image frame to which the detection boxes belong, wherein the client player does not display bullet screens in the masked region when playing the target video.

Abstract: A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Abstract: According to various embodiments, a pore visualization service providing server based on artificial intelligence may include a data pre-processor for obtaining a user's face image captured by a user terminal from the user terminal and performing pre-processing based on facial feature points based on the face image; a pore image extractor for generating a pore image corresponding to the user's face image by inputting the user's face image that has been pre-processed through the data pre-processing into an artificial neural network; a data post-processor for post-processing the generated pore image; and a pore visualization service providing unit for superimposing the post-processed pore image on the face image and transmitting a pore visualization image to the user terminal.

Abstract: Provided is a method for labeling a segment of a video, in a server. In the method, a multimedia file corresponding to an acting role is obtained. A role feature of the acting role is determined based on the multimedia file. A target video is decoded to obtain a data frame and a playing timestamp corresponding to the data frame, the data frame including at least one of a video frame and an audio frame. In the data frame of the target video, a target data frame that matches the role feature is identified. A segment related to performance of the acting role in the target video is automatically labeled based on a playing timestamp of the target data frame.

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for using inter-device communications and user permissions to capture and transmit digital images of users who have opted in for other computing devices capturing their images at an event. For example, the disclosed systems can receive registration information corresponding to a user attending an event, including whether the user opts in to having digital images of the user captured during the event. During the event, the disclosed systems can provide the current location of the user to other computing devices at the event. Upon detecting a trigger, one or more of the computing devices can capture (and encrypt) a digital image of the user and provide the digital image for access by the user.

Abstract: Described herein are methods and systems for remote visualization of real-time three-dimensional (3D) facial animation with synchronized voice. A sensor captures frames of a face of a person, each frame comprising color images of the face, depth maps of the face, voice data associated with the person, and a timestamp. The sensor generates a 3D face model of the person using the depth maps. A computing device receives the frames of the face and the 3D face model. The computing device preprocesses the 3D face model. For each frame, the computing device: detects facial landmarks using the color images; matches the 3D face model to the depth maps using non-rigid registration; updates a texture on a front part of the 3D face model using the color images; synchronizes the 3D face model with a segment of the voice data using the timestamp; and transmits the synchronized 3D face model and voice data to a remote device.

Abstract: Disclosed is a computer-implemented method for training a neural network system including an original neural network and a label generator. The method is based on an idea that the neural network system is trained by a sequence of training steps where at each training step at least one of a plurality of operations is performed and each of the operations gets performed at least once during training of the neural network system. Also disclosed are a neural network system and a computer program product.

Abstract: 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.

Abstract: A hardware system enables computer network interaction between network identity services and account-based service applications. In some examples, the network identity service biometrically identifies people. The network identity services and/or account-based service applications interact to exchange data in order to monitor, track, initiate, and/or cooperatively and/or individually perform various functions while protecting data that should not be shared and minimizing communication network usage and hardware and software resources. This interaction enables both to accomplish more and different tasks than they could individually while improving operational efficiency of hardware and software components of both.

Abstract: A terminal apparatus includes a controller configured to: transmit, to a server apparatus, a suggestion request requesting a suggestion of a function to be invoked; receive, from the server apparatus, a suggestion response that specifies a suggested function determined by the server apparatus; and invoke the suggested function. The suggestion request includes face image data of a user. The suggested function is determined by applying the face image data to a learned model. The controller is configured to: evaluate, based on face images of the user, a degree of change in facial expressions before and after invoking the suggested function; and transmit, to the server apparatus, a learning request requesting re-learning of the learned model when the evaluated degree of change in the facial expressions satisfies a predetermined condition.

Abstract: A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.

Abstract: A system for driver identification includes an interface and a processor. The interface is configured to receive a trip; and receive a set of albums, wherein an album of the set of albums includes one or more existing trips. The processor is configured to determine similarities for the trip to all trips in the set of albums; determine a first clustering based at least in part on the similarities; indicate that the trip is a provisionally assigned trip that is provisionally assigned to the album of the set of albums based at least in part on the first clustering; determine a second clustering for a set of provisionally assigned trips and all trips in the set of albums, wherein the set of provisionally assigned trips comprises qualified provisionally assigned trips; and determine updated trip assignments for an updated set of albums.

Abstract: Technologies are provided for assessing liveness of a subject presented to a set of one or more cameras. A first image and a second image depicting a face are captured during a first time period, and a third image and a fourth image depicting the face are captured during a second time period. A first variance in facial appearance is detected between the first depiction of the face and the third depiction of the face, and a second variance in facial appearance is detected between the second depiction of the face and the fourth depiction of the face. A liveliness of a person is verified based on a determination that the first variance in facial appearance corresponds in time to the second variance in facial appearance.

Abstract: Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory controller with various memory devices. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing request, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed “closer” to the memory devices, e.g., at the memory controller coupled to memory devices.

Abstract: The present disclosure provides a neural network model training method and apparatus, a living body detecting method and apparatus, a device and a storage medium. It is feasible to, with respect to different scenarios, respectively obtain picture samples under the scenarios, each picture sample having a tag, the tag including a living body or a non-living body and a corresponding scenario; and according to obtained picture samples, train to obtain a neural network model whose output is a feature vector which is discriminative for different scenarios. As such, when the living body detection is performed, it is possible to determine the feature vector of the to-be-detected picture according to the neural network model, then determine a picture sample matched with the to-be-detected picture, and regard the tag of the matched picture sample as the tag of the to-be-detected picture, thereby obtaining the detection result of the to-be-detected picture.

Abstract: A camera in a facility acquires an image of a user performing an action, such as shopping. These images may include potentially identifying information, such as an image that includes the user's face. To safeguard privacy of the user, a modified image is generated by changing one or more characteristics of the acquired image. The modification affects the entire image, rendering the resulting image unsuitable for another person to identify the user. For example, a contrast value for the image may be changed, rendering the user unrecognizable. This modification removes the potentially identifying information before later processing takes place. For example, the modified image in which the user's face is obscured may retain other non-identifying information used to train an artificial neural network to recognize actions such as pick or place of items in the facility.