Abstract: An electronic device and a method for controlling thereof are provided. A method for controlling an electronic device according to the disclosure includes obtaining a plurality of images for performing clustering, obtaining a plurality of target areas corresponding to each of the plurality of images, obtaining a plurality of feature vectors corresponding to the plurality of target areas, obtaining a plurality of central nodes corresponding to the plurality of feature vectors, obtaining neighbor nodes associated with each of the plurality of central nodes, obtaining a subgraph based on the plurality of central nodes and the neighbor nodes, identifying the connection probabilities between the plurality of central nodes of the subgraph and the neighbor nodes of each of the plurality of central nodes based on a graph convolutional network, and clustering the plurality of target areas based on the identified connection probabilities.

Abstract: In a data preparation process, raw data in a database is classified into first-type data and second-type data. In a data retrieval process, a first retrieval range is determined in the first-type data, and to-be-retrieved data is retrieved in the first retrieval range, to obtain a first retrieval result; the to-be-retrieved data is retrieved from all of the second-type data, to obtain a second retrieval result; and finally, a final retrieval result is determined from the first retrieval result and the second retrieval result.

Abstract: Systems and methods are presented for adaptive biometric authentication. According to one example method, a failure of a first type of biometric authentication is detected. In response to detecting the failure of the first type of biometric authentication, an alternative type of authentication is completed. In response to completing the alternative type of authentication, a biometric characteristic of a biometric signature is selectively modified in a stored user profile for processing a subsequent attempt of the first type of biometric authentication.

Abstract: Technologies are provided herein for selecting one or more subimages to be processed from a plurality of images received. A subset of images is identified, from the plurality of images received, that include a face image. The face image is assessed to generate assessment information regarding one or more of a size of the face image, a pose of the face in the face image, sharpness of the face image, and contrast quality of the face image. Each aspect of the assessment information is compared with corresponding criteria and, as a result of determining that the assessment information satisfies the criteria, facial recognition processing is performed on the face image.

Abstract: A support for image processing is provided, comprising: (a) detecting respective face regions from images consecutively photographed for a first person at predetermined time intervals by an image pickup unit to display images of the face regions detected in relation to the first person in a first region of a screen, and providing a user interface for indicating that a specific face image is selected from the face images of the first person displayed in the first region; (b) additionally displaying the specific face image through a second region adjacent to the first region; and (c) displaying a synthesized image using the specific face image as a representative face of the first person, when the specific face image displayed through the second region is selected.

Abstract: A method for augmenting point of interest in AR video. The method includes: extracting first frame of AR video depicting object being tracked including point of interest, by first pipeline; locating, in first frame set of feature points forming boundary of object; transferring frame with determined set of feature points to second pipeline; and by second pipeline determining coordinate system of set of feature points; calculating first location parameters of feature points; selecting first and second reference points from feature points, wherein first location parameters of point of interest are defined by first location parameters of first and second reference points; transmitting first location parameters of point of interest from second pipeline to first pipeline; and augmenting in first pipeline AR video with first location parameters of point of interest received from second pipeline.

Abstract: A learning-based model is trained using a plurality of attributes of media. Depth estimation is performed using the learning-based model. The depth estimation supports performing a computer vision task on the media. Attributes used in the depth estimation include scene understanding, depth correctness, and processing of sharp edges and gaps. The media may be processed to perform media restoration or the media quality enhancement. A computer vision task may include semantic segmentation.

Abstract: An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented automated face recognition program may be applied to the digital image to recognize, based on at least one feature vector from a prior-determined set of feature vectors, one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the prior-determined set of feature vectors may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

Abstract: Devices, systems, and methods are provided for enhanced video annotations using image analysis. A method may include identifying, by a first device, first faces of first video frames, and second faces of second video frames. The method may include determining a first score for the first video frames, the first score indicative of a first number of faces to label, the first number of faces represented by the first video frames, and determining a second score for the second video frames, the second score indicative of a second number of faces to label. The method may include selecting the first video frames for face labeling, and receiving a first face label for the first face. The method may include generating a second face label for the second faces. The method may include sending the first face label and the second face label to a second device for presentation.

Abstract: Embodiments of the disclosure disclose a method, apparatus, server, and storage medium for incorporating a structured entity, wherein the method for incorporating a structured entity can comprise: selecting a candidate entity associated with a to-be-incorporated structured entity from a knowledge graph, determining the to-be-incorporated structured entity being an associated entity based on prior attribute information of a category of the candidate entity and a preset model, merging the associated entity and the candidate entity, and incorporating the associated entity into the knowledge graph. The embodiments can select a candidate entity, and then integrate a preset model using prior knowledge, which can effectively improve the efficiency and accuracy in associating entities, and reduce the amount of calculation, to enable the structured entity to be simply and efficiently incorporated into the knowledge graph.

Abstract: This application provides a method for transmitting face image data and transferring value, apparatuses, an electronic device, and a storage medium, which belongs to the field of network technologies. The method for transmitting face image data includes acquiring a face data stream through the sensor, and transmitting the face data stream to the first processor; performing image screening on a face image in the face data stream by the first processor to obtain at least a target face image, the target face image meeting a target condition; retrieving a target web address from the memory by the first processor; and transmitting the target face image to the target web address by the first processor.

Abstract: An apparatus includes a motion detection unit configured to detect a moving object in an angle of view based on an event signal indicating a location of a pixel where a luminance change occurs and time when the luminance change occurs, a shape detection unit configured to detect a shape of the moving object from frame data generated based on the event signal, and a control unit configured to change the angle of view, wherein in a case where the detected moving object is not currently detected, the shape detection unit detects the shape of the moving object by changing the angle of view by a predetermined value.

Abstract: Systems, methods, and software are described herein for removing people distractors from images. A distractor mitigation solution implemented in one or more computing devices detects people in an image and identifies salient regions in the image. The solution then determines a saliency cue for each person and classifies each person as wanted or as an unwanted distractor based at least on the saliency cue. An unwanted person is then removed from the image or otherwise reduced from the perspective of being an unwanted distraction.

Abstract: Systems and techniques that facilitate real-time and/or offline de-identification of facial regions from regular and/or occluded color video streams obtained during diagnostic medical procedures are provided. A detection component can generate a bounding box substantially around a person in a frame of a video stream, can generate a heatmap showing key points or anatomical masks of the person based on the bounding box, and can localize a face or facial region of the person based on the key points or anatomical masks. An anonymization component can anonymize pixels in the frame that correspond to the face or facial region. A tracking component can track the face or facial region in a subsequent frame based on a structural similarity index between the frame and the subsequent frame being above a threshold.

Abstract: A shooting method and a terminal are provided. The method includes: receiving a first input performed by a user for M face images; in response to the first input, controlling a rotatable target camera to rotate and collect an image; performing facial recognition in a rotation process of the target camera; and performing at least one shooting operation and outputting at least one target image that includes M target persons, where each target person corresponds to one face image, and M is a positive integer greater than 1.

Abstract: A person detection device configured to analyze a fisheye image obtained by a fisheye camera installed above a to-be-detected area to detect a person existing in the to-be-detected area includes a human body detector configured to detect a human body candidate from a fisheye image and output, as a detection result, a bounding box indicating a region on the fisheye image of each human body candidate detected, a reference storage configured to prestore a reference for a shape and/or size of the bounding box for each position or area on the fisheye image, and a false detection determining unit configured to compare the shape and/or size of the bounding box of the human body candidate contained in the detection result with the reference corresponding to a position where the human body candidate is detected to determine whether the human body candidate results from false detection.

Abstract: This disclosure relates to systems, methods, and computer readable media for performing filtering of computer vision generated tags in a media file for the individual user in a multi-format, multi-protocol communication system. One or more media files may be received at a user client. The one or more media files may be automatically analyzed using computer vision models, and computer vision generated tags may be generated in response to analyzing the media file. The tags may then be filtered using Natural Language Processing (NLP) models, and information obtained during NLP tag filtering may be used to train and/or fine-tune one or more of the computer vision models and the NLP models.

Abstract: This disclosure relates to the use of “pseudo-images” to perform image recognition, e.g., to perform facial image recognition. In an embodiment, the pseudo-image is obtained by starting with a real world image and, after optional preprocessing, subjecting the image to a non-linear transformation that converts the image into a pseudo-image. While real world objects (or, more generally, real world patterns) may be perceivable in the starting image, they cannot be perceived in the pseudo-image. Image recognition takes place by comparing the pseudo-image with a library of known pseudo-images, i.e., image recognition takes place in pseudo-image space without a return to real world space. In this way, robust image recognition is achieved even for imperfect real world images, such as, real world images that have been degraded by noise, poor illumination, uneven lighting, and/or occlusion, e.g., the presence of glasses, scarves, or the like in the case of facial images.

Abstract: A computer-implemented method for video redaction of disclosure-protected objects is disclosed. A system for video redaction of disclosure-protected objects is also disclosed. A computer program product video redaction of disclosure-protected objects is also disclosed. A video interval may be broken into a plurality of sub-intervals, and unique random image substitution may be carried out over each of the sub-intervals in connection with the video redaction.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for accessing a set of images depicting at least a portion of a face. A set of facial regions of the face is identified, each facial region of the set of facial regions intersecting another facial region with at least one common vertex that is a member of a set of facial vertices. For each facial region of the set of facial regions, a weight formed from a set of region coefficients is generated. Based on the set of facial regions and the weight of each facial region of the set of facial regions, the face is tracked across the set of images.

Abstract: An image capturing support apparatus 1 includes: a detection unit 2 configured to detect facial images that have facial areas corresponding to a face, from a plurality of captured images, respectively; a determination unit 3 configured to determine whether or not the amount of light is insufficient for the detected facial images, using the detected facial images; and a superimposition unit 4 configured to, when the amount of light is insufficient, adjust the size of each of the facial images, and superimpose the facial areas of the adjusted facial images on each other.

Abstract: Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation.

Abstract: An embodiment vehicle includes a display device, an input device configured to obtain an input of a user, a smart key identifying device configured to search for a smart key of a plurality of smart keys, and a controller electrically connected to the display device, the input device, and the smart key identifying device, wherein the controller is configured to control the smart key identifying device to search for the smart key located inside the vehicle based on receiving a first command for executing a face registration process through the input device and to determine whether to proceed with the face registration process based on how many of the plurality of smart keys are identified as being inside the vehicle.

Abstract: A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.

Abstract: Examples described herein generally relate to processing a first image captured by the first camera at a first time to determine a first set of multiple key points of a face in the first image, processing a second image captured by the second camera at a second time, that is equal to or within a threshold time of the first time, to determine a second set of the multiple key points on the face in the second image, determining a location of at least a portion of the multiple key points in a three-dimensional space based on a first location of each of the portion of the multiple key points in the first image and a second location of each of the portion of the multiple key points in the second image, and detecting whether the face is a valid three-dimensional face for facial recognition based at least in part on the location of at least the portion of the multiple key points in the three-dimensional space.

Abstract: Systems and methods for digital image processing are described. Embodiments of the systems and methods includes identifying a digital image that includes an obscured region; generating a predicted image using a neural network comprising an encoder, a feature transfer network, and a decoder, wherein the feature transfer network is trained to predict features corresponding to the obscured region based on an output of the decoder; and combining the digital image and the predicted image to produce a reconstructed version of the digital image including details corresponding to the obscured region that are not present in the digital image.

Abstract: A method for pattern recognition may be provided, comprising: receiving data; processing the data with a convolutional neural network in order to recognize a pattern in the data, wherein the convolutional neural network comprises at least: a first branch comprising a sequence of first convolutional blocks, a pooling layer being disposed between any two adjacent first convolutional blocks, each first convolutional blocks comprising at least one convolutional layer, and a second branch comprising a sequence of second convolutional blocks, each second convolutional blocks comprising at least one convolutional layer, and wherein processing the data with a convolutional neural network in order to recognize a pattern in the data comprises: a preceding second convolutional block receiving a first feature map formed by combining of a feature map outputted by a preceding first convolutional block and a feature map outputted by a subsequent first convolutional block, processing the first feature map, and outputting a s

Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: An electric vehicle includes a steering element that can include a handle bar element. A platform supports a ride of the electric vehicle. At least one front wheel and at least one rear wheel are included and coupled to the platform. An electric motor provides a mechanical power to at least one of the front and rear wheels. A battery housing removably receives and holds one or more battery packs, where the one or more battery packs are configured to power the electric vehicle. A camera is included.

Abstract: A system associated with predicting segmentation quality of segmented objects implemented in the analysis of copious image data is disclosed. The system receives a collection of image data related to a particular type of data. The image data is segmented into segmented data portions based on an object associated with the collection of image data. Regions of interest associated with the segmented data portions are determined. The quality of segmentation of the segmented data portions is determined for respective classification of the regions of interest. A classification label is assigned to the regions of interest. Regions of interest are partitioned into sub-regions. Features associated with the sub-regions of the segmented data portions are determined. A training dataset is generated based on the determined features associated with the sub-regions in order to train a classification model based on a predetermined threshold value.

Abstract: A retrieval apparatus (2000) is accessible to a storage region (50) in which a plurality of pieces of object information (100) are stored. The object information (100) includes a feature value set (104) being a set of a plurality of feature values acquired regarding an object. The retrieval apparatus (2000) acquires a feature value set (retrieval target set (60)) being a retrieval target, and determines the object information (100) having the feature value set (104) similar to the retrieval target set (60) by comparing the retrieval target set (60) with the feature value set (104). Herein, in a case where a feature value set satisfies a predetermined condition, the retrieval apparatus (2000) performs comparison between the feature value set and another feature value set by using a part of feature values within the feature value set. Further, the retrieval apparatus (2000) outputs output information relating to the determined object information (100).

Abstract: This application provides a three-dimensional (3D) expression base generation method performed by a computer device. The method includes: obtaining image pairs of a target object in n types of head postures, each image pair including a color feature image and a depth image in a head posture; constructing a 3D human face model of the target object according to then image pairs; and generating a set of expression bases of the target object according to the 3D human face model of the target object. According to this application, based on a reconstructed 3D human face model, a set of expression bases of a target object is further generated, so that more diversified product functions may be expanded based on the set of expression bases.

Abstract: Disclosed is a method for annotation based on deep learning, which is performed by a computing device. The method may include: performing first learning of an agent model as supervised learning based on a first bounding box of an interest object corresponding to a ground truth (GT) for annotation; and performing second learning of the agent model as reinforcement learning based on a second bounding box of the interest object randomly sampled according to a constraint for geometric transform of a bounding box.

Abstract: A vehicle light signal detection and recognition method, system, and computer program product include bounding, using a coarse attention module, one or more regions of an image of an automobile including at least one of a brake light and a signal light generated by automobile signals which include illuminated sections to generate one or more bounded region, removing, using a fine attention module, noise from the one or more bounded regions to generate one or more noise-free bounded regions, and identifying the at least one of the brake light and the signal light from the one or more noise-free bounded regions.

Abstract: The present disclosure relates to an intelligent prevention passage control system for an electronic device, which comprises a detection device for an electronic device, a traffic control device integrated with the detection device for the electronic device, an industrial control host, a server, an access controller and a management terminal inside the traffic control device. The present disclosure can more accurately judge violations by scoring violations, and can make the system more intelligent by accurately judging violations, thus realizing no manual attendance, solving the problems of personnel recruitment, shift attendance and personnel management, and saving a lot of labor cost. At the same time, the system can avoid the hidden dangers caused by human feelings, sense of responsibility and inertia brought by manual prevention, and ensure the consistent, accurate and reliable standards of detection work.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image by illuminating a first object with a first light beam, determine a first measure of pixel values in the image and perform a comparison of the first measure of pixel values to a second measure of pixel values determined from a second image of a second object, wherein the second image is previously acquired by illuminating the second object with a second light beam. The instructions include further instructions to, when the comparison determines that the first measure is equal to the second measure of pixel values within a tolerance, determine that the first object and the second object are a same object.

Abstract: Disclosed is an electronic device. The electronic device includes a first housing, a second housing, and an input-output assembly. The second housing is arranged on a side opposite to a display screen of the electronic device. The first housing and the second housing are connected to define a receiving space. The second housing defines a light through hole. The input/output assembly is arranged on the first housing and received in the receiving space. A side of the first housing facing the second housing is arranged with a limiting member, and the limiting member is configured to fix the input-output assembly on the first housing. The input-output assembly includes a plurality of input-output modules including a laser transmitter, a laser receiver, and at least one image collector. Each input-output module faces the light through hole.

Abstract: A face recognition method includes: detecting keypoints when receiving a first face image; acquiring a recognition score of each detectable keypoint and serial numbers of missing keypoints; acquiring a plurality of target keypoints in the plurality of detectable keypoints having a predetermined face feature association relationship with the missing keypoints when the influence score is higher than a predetermined score threshold; acquiring a target face feature template having a degree of position combination with the plurality of target keypoints greater than a predetermined combination degree threshold; and stitching the target face feature template and the plurality of target keypoints on the first face image to obtain a second face image so as to detect all the keypoints according to the second face image for performing the face recognition.

Abstract: A medical image processing device having a processor configured to: acquire a medical image including an image of a subject; perform a first recognition of the medical image using a first recognizer; determine a confidence level for a recognition result of a first recognition by the first recognition; and perform a second recognition of the medical image using a second recognizer according to the confidence level for the recognition result of the first recognition, the second recognition having higher recognition accuracy than the first recognition.

Abstract: Systems and methods are described for dynamically adjusting an amount of retrieved recognition data based on the needs of a show, experience, or other event where participants are recognized. The retrieved recognition data may be deleted once it is no longer needed for the event. Recognition data retrieval is limited to just what is needed for the particular task, minimizing the uniqueness of any retrieved recognition data to respect participant privacy while providing an enhanced participant experience through recognition.

Abstract: A computer-implemented method is disclosed. The method includes: receiving, via an input interface associated with an electronic device, a first user input; identifying a first device class based on a current usage mode of the electronic device and a first set of device property values associated with the current usage mode of the electronic device; obtaining normalized values of input features associated with the first user input, the input feature values being normalized based on representative device property values that are defined for the first device class; and performing authentication of a user of the electronic device based on the normalized input feature values associated with the first user input.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Estimating emotion may include obtaining an image of at least part of a face, and applying, to the image, an expression convolutional neural network (“CNN”) to obtain a latent vector for the image, where the expression CNN is trained from a plurality of pairs each comprising a facial image and a 3D mesh representation corresponding to the facial image. Estimating emotion may further include comparing the latent vector for the image to a plurality of previously processed latent vectors associated with known emotion types to estimate an emotion type for the image.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: A method, a system, and a non-transitory computer readable medium for evaluating first movements of a first person in view of movements of a second person. The method may include determining the relationships between the first frames and the second frames by a sequence alignment algorithm, the determining of the relationships is based on the inter-frame similarity scores and the multiple state-related scores; and determining at least one evaluation score of the movements of the first person, based on the relationships and the multiple inter-frame similarity scores; wherein the at least one evaluation score is indicative of a similarity between the movements of the first person and the movements of the second person.

Abstract: The various embodiments described herein include methods, devices, and systems for categorizing motion events. In one aspect, a method is performed at a camera device. The method includes: (1) capturing a plurality of video frames via the image sensor, the plurality of video frames corresponding to a scene in a field of view of the camera; (2) sending the video frames to the remote server system in real-time; (3) while sending the video frames to the remote server system in real-time: (a) determining that motion has occurred within the scene; (b) in response to determining that motion has occurred within the scene, characterizing the motion as a motion event; and (c) generating motion event metadata for the motion event; and (4) sending the generated motion event metadata to the remote server system concurrently with the video frames.

Abstract: Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.

Abstract: Image processing systems and methods are provided for authorizing the performance at a computer terminal of an age-restricted activity. An estimated human age is determined based on human characteristics of a structure detected in an image captured at the computer terminal. It is determined whether the structure exhibits at least one liveness characteristic indicating the human characteristics from which the estimated human age is determined have been captured directly from a living human at the computer terminal. A positive determination is made as to whether performance of the age-restricted activity is authorized if the estimated human age meets a predetermined age requirement and the structure is determined to exhibit at least one liveness characteristic, and a negative determination is made if: i) the estimated human age does not meet the predetermined age requirement; and/or ii) the structure is not determined to exhibit at least one liveness characteristic.

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 electronic device for setting a processing procedure for controlling an apparatus, the electronic device comprising: at least one processor configured to cause the electronic device to perform operations of: obtaining a captured image; determining a type of an object included in the captured image; notifying an item indicating target processing corresponding to the object included in the captured image, among a plurality of analysis processing; notifying a result obtained by applying the target processing to the captured image; and receiving a user instruction for adding the processing corresponding to the item to a processing procedure for controlling the apparatus.