Abstract: A gaze estimation method and apparatus is disclosed. The gaze estimation method includes obtaining an image including an eye region of a user, extracting, from the obtained image, a first feature of data, obtaining a second feature of data used for calibration of a neural network model, and estimating a gaze of the user using the first feature and the second feature.

Abstract: A processor implemented method of processing a facial expression image, the method includes controlling a camera to capture a first facial expression image and a second facial expression image, acquiring a first expression feature of the first facial expression image, acquiring a second expression feature of the second facial expression image, generating a new expression feature dependent on differences between the acquired first expression feature and the acquired second expression feature, and adjusting a target facial expression image based on the new expression feature.

Abstract: A gaze estimation method includes receiving, by a processor, input data including a current image and a previous image each including a face of a user, determining, by the processor, a gaze mode indicating a relative movement between the user and a camera that captured the current image and the previous image based on the input data, and estimating, by the processor, a gaze of the user based on the determined gaze mode, wherein the determined gaze mode is one of a plurality of gaze modes comprising a stationary mode and a motion mode.

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: A gaze estimation method and apparatus is disclosed. The gaze estimation method includes obtaining an image including an eye region of a user, extracting, from the obtained image, a first feature of data, obtaining a second feature of data used for calibration of a neural network model, and estimating a gaze of the user using the first feature and the second feature.

Abstract: A gaze estimation method and apparatus is disclosed. The gaze estimation method includes obtaining an image including an eye region of a user, extracting, from the obtained image, a first feature of data, obtaining a second feature of data used for calibration of a neural network model, and estimating a gaze of the user using the first feature and the second feature.

Abstract: An image processing method and apparatus using a neural network are provided. The image processing method includes generating a plurality of augmented features by augmenting an input feature, and generating a prediction result based on the plurality of augmented features.

Abstract: A processor implemented method of processing a facial expression image, the method includes controlling a camera to capture a first facial expression image and a second facial expression image, acquiring a first expression feature of the first facial expression image, acquiring a second expression feature of the second facial expression image, generating a new expression feature dependent on differences between the acquired first expression feature and the acquired second expression feature, and adjusting a target facial expression image based on the new expression feature.

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: 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 gaze estimation method and apparatus is disclosed. The gaze estimation method includes obtaining an image including an eye region of a user, extracting, from the obtained image, a first feature of data, obtaining a second feature of data used for calibration of a neural network model, and estimating a gaze of the user using the first feature and the second feature.

Abstract: A gaze estimation method includes receiving, by a processor, input data including a current image and a previous image each including a face of a user, determining, by the processor, a gaze mode indicating a relative movement between the user and a camera that captured the current image and the previous image based on the input data, and estimating, by the processor, a gaze of the user based on the determined gaze mode, wherein the determined gaze mode is one of a plurality of gaze modes comprising a stationary mode and a motion mode.

Abstract: A method to analyze a facial image includes: inputting a facial image to a residual network including residual blocks that are sequentially combined and arranged in a direction from an input to an output; processing the facial image using the residual network; and acquiring an analysis map from an output of an N-th residual block among the residual blocks using a residual deconvolution network, wherein the residual network transfers the output of the N-th residual block to the residual deconvolution network, and N is a natural number that is less than a number of all of the residual blocks, and wherein the residual deconvolution network includes residual deconvolution blocks that are sequentially combined, and the residual deconvolution blocks correspond to respective residual blocks from a first residual block among the residual blocks to the N-th residual block.

Abstract: A user trigger intent determining method and apparatus is disclosed. The user trigger intent determining apparatus may obtain a first face image, obtain a second face image after a visual stimuli object is displayed, and determine a final gaze location by correcting a first gaze location estimated from the first face image based on a second gaze location estimated from the second face image.

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 method to analyze a facial image includes: inputting a facial image to a residual network including residual blocks that are sequentially combined and arranged in a direction from an input to an output; processing the facial image using the residual network; and acquiring an analysis map from an output of an N-th residual block among the residual blocks using a residual deconvolution network, wherein the residual network transfers the output of the N-th residual block to the residual deconvolution network, and N is a natural number that is less than a number of all of the residual blocks, and wherein the residual deconvolution network includes residual deconvolution blocks that are sequentially combined, and the residual deconvolution blocks correspond to respective residual blocks from a first residual block among the residual blocks to the N-th residual block.