Beihang University

Yiwei Bao

Feng Lu

Qinping Zhao

The embodiments of the present disclosure disclose a method for gaze estimation based on physically interpretable features. A specific mode of implementation of this method comprises: constructing a first convolutional neural network, inputting a user's facial image, extracting image features, and regression fitting to obtain an estimated value of the gaze direction; transforming the image features extracted by the first convolutional neural network into physically interpretable features through data processing algorithms, and performing gaze estimation through physically interpretable features; training a gaze estimation model based on physically interpretable features, and on the basis of the trained first convolutional neural network, continuing to optimize the first convolutional neural network using the physically interpretable features.

Method for gaze estimation based on physically interpretable features

The present application provides a gaze point estimation method, device, and an electronic device. The method includes: acquiring user image data; acquiring a facial feature vector according to a preset first convolutional neural network and the facial image; acquiring a position feature vector according to a preset first fully connected network and the position data; acquiring a binocular fusion feature vector according to a preset eye feature fusion network, the left-eye image and the right-eye image; and acquiring position information about a gaze point of a user according to a preset second fully connected network, the facial feature vector, the position feature vector, and the binocular fusion feature vector. In this technical solution, relation between eye images and face images is utilized to achieve accurate gaze point estimation.

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BEIJING, China

Yiwei Bao