Abstract: A method, apparatus, and electronic device for processing a video, a medium and a product are presented. An implementation of the method includes: acquiring a target video; selecting, from at least one preset model, a preset model as a target model; determining output data of the target model based on the target video and the target model; reselecting, in response to determining that the output data does not meet a condition corresponding to the target model, another preset model as the target model from the at least one preset model until the output data of the target model meets the condition corresponding to the target model; and determining, based on the output data, a dynamic cover from the target video.

Abstract: A system for calibrating a three-dimensional scanning device includes a structured-light scanner capable of performing a structured-light operation, and a processor that performs calibration on a device under calibration (DUC). The structured-light scanner captures a base image by performing the structured-light operation prior to calibration. The structured-light scanner captures a calibration image with respect to corresponding DUC during calibration, and the calibration image is inputted to the processor, which determines transformation mapping from the calibration image to the base image. The determined transformation is then transferred to the DUC during calibration.

Abstract: A facial recognition method using online sparse learning includes initializing target position and scale, extracting positive and negative samples, and extracting high-dimensional Haar-like features. A sparse coding function can be used to determine sparse Haar-like features and form a sparse feature matrix, and the sparse feature matrix in turn is used to classify targets.

Abstract: An information processing apparatus (100) includes a collation unit (102) that collates first feature information extracted from a person included in a first image (20) with first registered feature information stored in a storage unit (110), and a registration unit (104) that stores, in a case where the first feature information is unable to be extracted from the person or a collation result in the collation unit (102) indicates a mismatch, second feature information extracted from the person in the storage unit (110) as second registered feature information, in which the collation unit (102) collates second feature information extracted from a person included in a second image (22) with the second registered feature information stored in the storage unit (110), and thus specifies the person corresponding to the second registered feature information in the second image (22).

Abstract: Described are systems and methods for generating personalized videos with customized text messages. An example method commences with receiving an input text and a video template. The video template includes a sequence of frame images and text parameters defining an animation of the input text for the sequence of frame images. The method continues with rendering an output video. The output video includes the sequence of frame images featuring the input text rendered according to the text parameters. The method further includes providing a user with an option to change at least one text parameter of the text parameters. The method continues with dynamically changing, by the at least one computing resource, the input text according to the at least one text parameter. The method further includes providing, by the at least one computing resource, the output video to at least one further computing resource via a communication chat.

Abstract: Vehicle event detection and reporting systems and methods include sensor systems that capture event data from which events involving the vehicle are determined, the sensor systems including a driver facing camera that captures images of a driver's seat area. The driver facing camera also captures images of a barcode presented to the driver facing camera, and the detected events are associated with a driver identification determined from the captured image of the barcode.

Abstract: An apparatus for face image candidate determination for authentication, including a storage and at least one processor wherein the storage stores a reference face image in association with an attribute of the image for each registered person and a score threshold value for each combination of attributes of face images to be collated. The processor configured to: acquire a face image of a person to be determined as a face image for determination, determine an attribute of the face image, collate the reference face image with the face image and calculates a score indicating a degree of matching, and select a reference face image satisfying the score threshold value as a face image candidate for authentication and selects the face image candidate and the score thereof as authentication information of the face image for determination.

Abstract: Methods, systems, and media for analyzing spherical video content are provided. More particularly, methods, systems, and media for detecting two-dimensional videos placed on a sphere in abusive spherical video content are provided.

Abstract: Methods and systems for image clustering are described, and include first receiving a plurality of images from a video of a game. Next, determining a first feature vector of a first player from the plurality of images by inputting the plurality of images to an artificial intelligence (AI)-based process. Next, dividing the plurality of images into a first subset of images and a second subset of images based on the first feature vector. Next, receiving a first identifier tagging the first player in a first image of the first subset. Finally, identifying a second player in a second image in the second subset by propagating the first identifier of the first subset, based on a distance measure associated with the first feature vector from the artificial intelligence (AI)-based process. Running on a mobile computing device, the method allows multiplayer tagging to be easily performed in almost any environment.

Abstract: A processor-implemented facial image generating method includes: determining a first feature vector associated with a pose and a second feature vector associated with an identity by encoding an input image including a face; determining a flipped first feature vector by flipping the first feature vector with respect to an axis in a corresponding space; determining an assistant feature vector based on the flipped first feature vector and rotation information corresponding to the input image; determining a final feature vector based on the first feature vector and the assistant feature vector; and generating an output image including a rotated face by decoding the final feature vector and the second feature vector based on the rotation information.

Abstract: The present disclosure provides a method for training a convolutional neural network and a method and device for face recognition. By arranging a first training sample set including a first data set and a second data set, the convolutional neural network is trained and may be applied to the face recognition method, an initial face image frame may be extracted from a face detection image, in the face recognition method, a portion above eyes is cropped from the initial face image frame to serve as a target face detection image, thus according to the target face detection image, predicted identity information corresponding to the target face detection image is determined, and then the target face detection image is compared with a face reference image corresponding to the predicted identity information for face recognition.

Abstract: A face authentication apparatus includes a face image acquisition unit, a collation unit, a time measurement unit, and a threshold change unit. The face image acquisition unit acquires a face image of an authentication target. The collation unit performs face authentication on the face image of the authentication target on the basis of a threshold. The time measurement unit measures the elapsed time from the time of starting operation of the face authentication apparatus. The threshold change unit restricts changing of the threshold to a value larger than a value determined depending on the measured elapsed time.

Abstract: The present invention concerns a method for learning the parameters of a convolutional neural network, CNN, for data classification, the method comprising the implementation of steps by data processing means (11a, 11 b, 11c) of at least one server (1a, 1b, 1c), of: (a1) Learning, from a base of already-classified confidential learning data, the parameters of a first CNN; (a2) Learning, from a base of already-classified public learning data, the parameters of a last fully-connected layer (FC) of a second CNN corresponding to the first CNN to which said fully-connected layer (FC) has been added. The present invention also concerns a method for classifying an input datum.

Abstract: Disclosed are systems and methods for personalized videos. An example method include receiving, by a computing device, preprocessed videos including a target face, providing, by the computing device, a first user interface enabling a user to generate an image of a source face, modifying, by the computing device, the preprocessed videos to generate one or more personalized videos by replacing the target face with the source face, the source face being modified to adopting a facial expression of the target face, and providing, by the computing device, a second user interface to select the one or more personalized videos.

Abstract: Technology disclosed herein may involve a computing system that (i) based on an image of a target object of a given class of object and at least one GAN configured to generate artificial images of the given class of object, generates an artificial image of the target object that is substantially similar to real-world images of objects of the given class of objects captured by real-world scanning devices, (ii) based on an image of a receptacle, selects an insertion location within the receptacle in the image of the receptacle to insert the artificial image of the target object, (iii) generates a combined image of the receptacle and the target object, wherein generating the combined image comprises inserting the artificial image of the target object into the image of the receptacle at the insertion location, and (iv) trains one or more object detection algorithms with the combined image of the receptacle and the target object.

Abstract: A frictionless access control system and method embodying satellite cameras for facial recognition are disclosed. The cameras capture image data of individuals at an access point such as a door. Preferably, two or more cameras are placed on opposite sides of the access point to increase the likelihood that the individuals are captured in the image data. A facial cropper module extracts facial patches from the image data, and a facial signature module computes facial signatures from the facial patches. A facial recognition module receives the computed facial signatures from the facial signature module, matches the computed facial signatures to stored facial signatures, and sends user identity information of individuals corresponding to the stored facial signatures to the facial signature module when the computed facial signatures match the stored facial signatures.

Abstract: Imputation of missing data can be provided for both normalized and non-normalized data. Data can be partitioned, and data complete records used to impute values for data missing records. Hierarchical clustering can be used to cluster the data. Imputation can rely on a closed record in a same cluster. Record similarity can be used to adjust an observed value, and an estimated mean can also be incorporated. Useful for augmenting datasets that can be applied to analytics, machine learning, and other scenarios.

Abstract: A face authentication apparatus includes a face image acquisition unit, a collation unit, a time measurement unit, and a threshold change unit. The face image acquisition unit acquires a face image of an authentication target. The collation unit performs face authentication on the face image of the authentication target on the basis of a threshold. The time measurement unit measures the elapsed time from the time of starting operation of the face authentication apparatus. The threshold change unit restricts changing of the threshold to a value larger than a value determined depending on the measured elapsed time.

Abstract: A face authentication apparatus includes a face image acquisition unit, a collation unit, a time measurement unit, and a threshold change unit. The face image acquisition unit acquires a face image of an authentication target. The collation unit performs face authentication on the face image of the authentication target on the basis of a threshold. The time measurement unit measures the elapsed time from the time of starting operation of the face authentication apparatus. The threshold change unit restricts changing of the threshold to a value larger than a value determined depending on the measured elapsed time.

Abstract: Disclosed are a method and device for predicting face beauty based on migration and weak supervision and a storage medium. The method includes: preprocessing an inputted face image; training a source domain network by using the preprocessed image, and migrating a parameter of the source domain network to a target domain network; inputting a noise image marked with a noise label and a truth-value image marked with a truth-value label into the target domain network to obtain an image feature; and inputting the image feature into a classification network to obtain a final face beauty prediction result.