Abstract: A gaze tracking method and apparatus, and a gaze tracking neural network training method and apparatus are provided. The gaze tracking apparatus includes one or more processors and a memory, and the one or more processors obtain output position information from an input face image of a user using a neural network model, determines a position adjustment parameter for the user, and predicts gaze position information of the user by adjusting the output position information based on the position adjustment parameter.

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: A facial verification method includes separating a query face image into color channel images of different color channels, obtaining a multi-color channel target face image with a reduced shading of the query face image based on a smoothed image and a gradient image of each of the color channel images, extracting a face feature from the multi-color channel target face image, and determining whether face verification is successful based on the extracted face feature.

Abstract: A processor-implemented method includes obtaining an input image including a face of a user, extracting a feature from the input image, estimating a facial expression of the input image and an expressiveness of the facial expression of the input image corresponding to a determined expression intensity of the facial expression based on the extracted feature, normalizing the expressiveness of the facial expression of the input image to a universal expressiveness, and recognizing the facial expression based on the universal expressiveness.

Abstract: A liveness test method and apparatus is disclosed. A processor implemented liveness test method includes extracting an interest region of an object from a portion of the object in an input image, performing a liveness test on the object using a neural network model-based liveness test model, the liveness test model using image information of the interest region as provided first input image information to the liveness test model and determining liveness based at least on extracted texture information from the information of the interest region by the liveness test model, and indicating a result of the liveness test.

Abstract: An object recognition apparatus, a classification tree learning apparatus, an operation method of the object recognition apparatus, and an operation method of the classification tree learning apparatus are provided. The object recognition apparatus 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, using a classification tree.

Abstract: A user registration device and method is disclosed. The user registration device compares an initial image stored in a database and a newly input candidate image, and determines whether to generate and manage an additional database based on the similarity between the initial image and the candidate image.

Abstract: A method and apparatus with an adaptively updated enrollment database (DB) are provided. A method with an adaptively updated enrollment database (DB) includes extracting an input feature vector from an input image, determining whether the input feature vector is included in a changeable enrollment range, with the changeable enrollment range being determined based on a threshold distance from each of plural enrolled feature vectors in the enrollment DB, and with the enrolled feature vectors corresponding to enrolled images, determining whether to enroll the input feature vector in the enrollment DB in response to the input feature vector being determined as being included in the changeable enrollment range, and in response to a result of the determining of whether to enroll the input feature vector being to enroll the input feature vector, selectively enrolling the input feature vector in the enrollment DB.

Abstract: A processor implemented method of processing a facial expression image, the method includes acquiring an expression feature of each of at least two reference facial expression images; generating a new expression feature based on an interpolation value of the expression feature; and adjusting a target facial expression image based on the new expression feature and creating a new facial expression image.

Abstract: A fingerprint recognition method includes determining a code corresponding to a query image based on features of blocks in the query image, obtaining information corresponding to the determined code from a lookup table, and verifying the query image based on the obtained information.

Abstract: An object recognition apparatus and method are provided. The apparatus includes a processor configured to verify a target image using an object model and based on reference intermediate data extracted by a partial layer of the object model as used in an object recognition of an input image, in response to a failure of a verification of the input image after a success of the object recognition of the input image, and perform an additional verification of the target image in response to the target image being verified in the verifying of the target image.

Abstract: A convolutional neural network (CNN) processing method and apparatus. The apparatus may select, based on at least one of a characteristic of at least one kernel of a convolution layer or a characteristic of an input of the convolution layer, one operation mode from a first operation mode reusing a kernel, of the at least one kernel, and a second operation mode reusing the input, and perform a convolution operation based on the selected operation mode.

Abstract: An electronic device includes a printed circuit board (PCB) defining a cavity, a first component pad of the PCB positioned outside the cavity, and a second component pad of the PCB positioned on a bottom surface of the cavity. The first component pad has a first thickness, and the second component pad has a second thickness that is less than the first thickness of the first component pad. An electronic component, such as a surface mounted technology (SMT) component, is mounted to the second component pad within the cavity.

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: A method of displaying a menu based on at least one of a depth information and a space gesture is provided. The method including determining depth information corresponding to a distance from a screen of a user terminal to a hand of a user; identifying at least one layer among a plurality of layers based on the depth information; and applying a graphic effect to the identified layer so that a menu page corresponding to the at least one identified layer is displayed on the screen of the user terminal.

Abstract: A liveness test method and apparatus is disclosed. A processor implemented liveness test method includes extracting an interest region of an object from a portion of the object in an input image, performing a liveness test on the object using a neural network model-based liveness test model, the liveness test model using image information of the interest region as provided first input image information to the liveness test model and determining liveness based at least on extracted texture information from the information of the interest region by the liveness test model, and indicating a result of the liveness test.

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: A fingerprint recognition based authentication method and apparatus is disclosed. The authentication apparatus may obtain an input fingerprint from a touch input of a user, determine an input number corresponding to the input fingerprint using preregistered fingerprint-number mapping information, and authenticate the user based on whether an input number sequence corresponding to an input fingerprint sequence is identical to a reference number sequence.

Abstract: A method to reduce a neural network includes: adding a reduced layer, which is reduced from a layer in the neural network, to the neural network; computing a layer loss and a result loss with respect to the reduced layer based on the layer and the reduced layer; and determining a parameter of the reduced layer based on the layer loss and the result loss.

Abstract: A convolutional neural network (CNN) processing method and apparatus. The apparatus may select, based on at least one of a characteristic of at least one kernel of a convolution layer or a characteristic of an input of the convolution layer, one operation mode from a first operation mode reusing a kernel, of the at least one kernel, and a second operation mode reusing the input, and perform a convolution operation based on the selected operation mode.