Abstract:
An object recognition system is provided. The object recognition system for recognizing an object may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, by using a classification tree. The object recognition system may include a classification tree learning apparatus to generate the classification tree.
Abstract:
An apparatus for processing a depth image using a relative angle between an image sensor and a target object includes an object image extractor to extract an object image from the depth image, a relative angle calculator to calculate a relative angle between an image sensor used to photograph the depth image and a target object corresponding to the object image, and an object image rotator to rotate the object image based on the relative angle and a reference angle.
Abstract:
Disclosed is a method and apparatus for adaptive tracking of a target object. The method includes method of tracking an object, the method including estimating a dynamic characteristic of an object in an input image based on frames of the input image, determining a size of a crop region for a current frame of the input image based on the dynamic characteristic of the object, generating a cropped image by cropping the current frame based on the size of the crop region, and generating a result of tracking the object for the current frame using the cropped image.
Abstract:
A processor-implemented liveness test method includes detecting a face region in a query image, the query image including a test object for a liveness test, determining a liveness test condition to be applied to the test object among at least one liveness test condition for at least one registered user registered in a registration database, determining at least one test region in the query image based on the detected face region and the determined liveness test condition, obtaining feature data of the test object from image data of the determined at least one test region using a neural network-based feature extractor, and determining a result of the liveness test based on the obtained feature data and registered feature data registered in the registration database and corresponding to the determined liveness test condition.
Abstract:
Disclosed is a target tracking method and apparatus. The target tracking apparatus includes a processor configured to obtain a first depth feature from a target region image and obtain a second depth feature from a search region image, obtain a global response diagram between the first depth feature and the second depth feature, acquire temporary bounding box information based on the global response diagram, updated the second depth feature based on the temporary bounding box information, obtain local feature blocks based on the first depth feature, obtain a local response diagram based on the local feature blocks and the updated second depth feature, and determine output bounding box information based on the local response diagram.
Abstract:
Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.
Abstract:
A method with liveness testing may include: acquiring an infrared (IR) image including an object, and a depth image including the object; generating a first preprocessed IR image by performing first edge enhancement preprocessing on the IR image; generating a preprocessed depth image by performing second edge enhancement preprocessing on the depth image; and determining whether the object is a genuine object based on the first preprocessed IR image and the preprocessed depth image.
Abstract:
Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.
Abstract:
A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.
Abstract:
A facial expression recognition apparatus and method and a facial expression training apparatus and method are provided. The facial expression recognition apparatus generates a speech map indicating a correlation between a speech and each portion of an object based on a speech model, extracts a facial expression feature associated with a facial expression based on a facial expression model, and recognizes a facial expression of the object based on the speech map and the facial expression feature. The facial expression training apparatus trains the speech model and the facial expression model.