Abstract: The present invention relates to a method and apparatus for re-registering pre-registered identity information in a new identity recognition system. A method of re-registering identity feature information in an identity recognition system according to an embodiment of the present disclosure may include: identifying one or more first identity feature information calculated by the first feature calculation device and stored in a database; generating one or more latent space information by inputting the one or more first identity feature information into a specific model for latent space estimation; generating one or more second identity feature information by inputting the one or more latent space information into a second feature calculation device; and storing the one or more second identity feature information in a new database.

Abstract: The present invention relates to a method and apparatus for re-registering pre-registered identity information in a new identity recognition system. A method of re-registering identity feature information in an identity recognition system according to an embodiment of the present disclosure may include: identifying one or more first identity feature information calculated by the first feature calculation device and stored in a database; generating one or more latent space information by inputting the one or more first identity feature information into a specific model for latent space estimation; generating one or more second identity feature information by inputting the one or more latent space information into a second feature calculation device; and storing the one or more second identity feature information in a new database.

Abstract: Provided are a device and method for tagging training data. The method includes detecting and tracking one or more objects included in a video using artificial intelligence (AI), when there is an object to be split in a result of tracking the detected objects, splitting the object in object units, and when there are identical objects to be merged among split objects, merging the objects.

Abstract: Provided are a device and method for tagging training data. The method includes detecting and tracking one or more objects included in a video using artificial intelligence (AI), when there is an object to be split in a result of tracking the detected objects, splitting the object in object units, and when there are identical objects to be merged among split objects, merging the objects.

Abstract: Disclosed herein a method and apparatus for recommending a table service based on image recognition. According to an embodiment of the present disclosure, there is provided a method for recommending a table service, including: receiving a table image that is captured in real time; acquiring, by using an artificial intelligence of a pre-learned learning model, table information that includes object information and food information of at least one table in the table image; and recommending, based on the table information, a service for each of the at least one table.

Abstract: Disclosed herein a method and apparatus for learning a locally-adaptive local device task based on cloud simulation. According to an embodiment of the present disclosure, there is provided a method for learning a locally-adaptive local device task. The method comprising: receiving observation data about a surrounding environment recognized by a local device; performing a domain randomization based on the observation data and a failure type of a task assigned to the local device and relearning a policy network of the assigned task based on the domain randomization; and updating a policy network of the local device for the assigned task by transmitting the relearned policy network to the local device.

Abstract: Provided are a human-tracking method and a robot apparatus. The human-tracking method includes receiving an image frame including a color image and a depth image, determining whether user tracking was successful in a previous image frame, and determining a location of a user and a goal position to which a robot apparatus is to move based on the color image and the depth image in the image frame, when user tracking was successful in the previous frame. Accordingly, a current location of the user can be predicted from the depth image, user tracking can be quickly performed, and the user can be re-detected and tracked using user information acquired in user tracking when detection of the user fails due to obstacles or the like.

Abstract: An apparatus includes an image receiving module configured to collect a depth image provided from a camera, a human body detection module configured to detect a human body from the collected depth image, and an activity recognition module configured to recognize an action of the human body on the basis of a 3-dimensional action volume extracted from the human body and a previously learned action model.

Abstract: Provided is a gesture recognition apparatus. The gesture recognition apparatus includes a human detection unit, a gesture region setting region, an arm detection unit and a gesture determination unit. The human detection unit detects a face region of a user from an input image. The gesture region setting unit sets a gesture region, in which a gesture of the user's arm occurs, with respect to the detected face region. The arm detection unit detects an arm region of the user in the gesture region. The gesture determination unit analyzes a position, moving directionality and shape information of the arm region in the gesture region to determine a target gesture of the user. Such a gesture recognition apparatus may be used as a useful means for a human-robot interaction in a long distance where a robot has difficulty in recognizing a user's voice.

Abstract: A method and apparatus for proving sign information are disclosed. The sign information providing method includes: extracting a first sign from an input image, wherein the first sign is pre-defined; extracting a second sign representing information corresponding to the first sign around the location of the first sign, from the input image; and providing at least one piece of information of information about the first sign and information about the second sign in the form of voice. Accordingly, a user may correctly recognize information expressed by a sign.

Abstract: An apparatus includes an image receiving module configured to collect a depth image provided from a camera, a human body detection module configured to detect a human body from the collected depth image, and an activity recognition module configured to recognize an action of the human body on the basis of a 3-dimensional action volume extracted from the human body and a previously learned action model.

Abstract: A method includes matching a pupil center position obtained from image information taken by a camera and a center position of an UI on a display panel of a terminal, and recognizing the match as a touch on the UI when the match between the pupil center position and the center position of the UI is kept for a predetermined time or more.

Abstract: An apparatus for discriminating a disguised face includes a face area detector configured to detect a face area in an input image provided from an external source. The apparatus includes a skin color modeling module configured to separate a skin color area from the face area and a disguised face discriminator configured to determine whether signals in the skin color area have a pulse component to discriminate whether a face in the input image is the disguised face.

Abstract: Disclosed is a method of sequencing character information in order to increase precision of character recognition. The method includes: a pre-processing that extracts character information from an image to binarize the extracted character information through a predetermined threshold and extracts and thins a center line of the binarized character information; normalizing the pre-processed character information to character information according to a predetermined criteria; and sequencing the normalized character information using structural features including an end point or a divergence point of the character information. The present invention suggests an angle normalization method of input character information, a structural feature position determining method, and a structural feature numeral string generating method to strongly recognize characters configured by various fonts obtained from a natural scene regardless of an angle or a size of the characters.

Abstract: Disclosed are a system and a method for recognizing a disguised face using a Gabor feature and a support vector machine (SVM) classifier according to the present invention. The system for recognizing a disguised face includes: a graph generation means to generate a single standard face graph from a plurality of facial image samples; a support vector machine (SVM) learning means to determine an optimal classification plane for discriminating a disguised face from the plurality of facial image samples and disguised facial image samples; and a facial recognition means to determine whether an input facial image is disguised using the standard face graph and the optimal classification plane when the facial image to be recognized is input.

Abstract: A method for tracking an object in an object tracking apparatus includes receiving an image frame of an image; and detecting a target, a depth analogous obstacle and an appearance analogous obstacle; tracking the target, the depth analogous obstacle and the appearance analogous obstacle; when the detected target overlaps the depth analogous obstacle, comparing the variation of tracking score of the target with that of the depth analogous obstacle. Further, the method includes continuously tracking the target when the variation of tracking score of the target is below that of the depth analogous obstacle and processing a next frame when the variation of tracking score of the target is above that of the depth analogous obstacle; and re-detecting the target.

Abstract: A human information recognition method includes analyzing sensor data from multi-sensor resource placed in a recognition space to generate human information based on the sensor data, the human information including an identity, location and activity information of people existed in the recognition space. Further, the human information recognition method includes mixing the human information based on the sensor data with human information, the human information being acquired through interaction with the people existed in the recognition space; and storing a human model of the people existed in the recognition space depending on the mixed human information in a database unit.

Abstract: Provided is a multiple-intelligence detection system. The multiple-intelligence detection system includes an image detection device obtaining image information for evaluating multiple-intelligence from a user, a multiple-intelligence measurement model unit receiving the image information from the image detection device to perform multiple-intelligence evaluation through selection of one of a first reaction and a second reaction, and a content unit receiving a result of the evaluated multiple-intelligence from the multiple-intelligence measurement model unit to generate an individual portfolio on the basis of the received result. The multiple-intelligence measurement model unit selects one of the first and second reactions on the basis of a reference reaction according to feelings and behavior patterns of the user.

Abstract: Disclosed is a method of detecting an upper body. The method includes detecting an omega candidate area including a shape formed of a face and a shoulder line of a human from a target image, cutting the target image into the upper body candidate area including the omega candidate area, detecting a human face from the upper body candidate area, and judging whether the upper body of the human is included in the target image according to the result of detecting the human face.

Abstract: Provided are a human-tracking method and a robot apparatus. The human-tracking method includes receiving an image frame including a color image and a depth image, determining whether user tracking was successful in a previous image frame, and determining a location of a user and a goal position to which a robot apparatus is to move based on the color image and the depth image in the image frame, when user tracking was successful in the previous frame. Accordingly, a current location of the user can be predicted from the depth image, user tracking can be quickly performed, and the user can be re-detected and tracked using user information acquired in user tracking when detection of the user fails due to obstacles or the like.