Generating gaze corrected images using bidirectionally trained network
Abstract:
An example apparatus for correcting gaze in images includes an image receiver to receive an image comprising an eye and a target angle set to a center. The apparatus also includes a bidirectionally trained convolutional neural network (CNN) to receive the image and the target angle from the image receiver and generate a vector field and a brightness map based on the image and the target angle. The apparatus further includes an image corrector to generate a gaze corrected image based on the vector field and the brightness map.
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