Online education man-machine interaction method and system based on artificial intelligence
A technology of human-computer interaction and artificial intelligence, applied in the field of human-computer interaction in online education, can solve problems such as the inability to determine the user's progress in class, the inability to evaluate the teacher's teaching quality, and the inability of the teacher to grasp it.
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Embodiment 1
[0101] The human-computer interaction method for online education based on artificial intelligence provided by the embodiment of the present invention is as follows: figure 1 As shown, as a preferred embodiment, such as image 3 As shown, the method for feature extraction of the processed user facial image by the image feature extraction module provided by the embodiment of the present invention includes:
[0102] S201. Acquire the processed user's facial image, segment the user's facial area image, and obtain images corresponding to each preset facial sub-area; for each preset facial sub-area, use the first convolutional neural network to extract The image space feature information in each image corresponding to the preset facial sub-region, and according to the time sequence of each image corresponding to the preset facial sub-region;
[0103] S202, sequentially input the image space feature information of each image corresponding to the preset facial sub-region into the cy...
Embodiment 2
[0109] The human-computer interaction method for online education based on artificial intelligence provided by the embodiment of the present invention is as follows: figure 1 As shown, as a preferred embodiment, such as Figure 4 As shown, the method for emotion classification and recognition based on the extracted image features provided by the embodiment of the present invention includes:
[0110] S301, obtaining the image feature points contained in the facial image feature set and determining the position information of each facial image feature point, and simultaneously performing normalization processing on the extracted image features; calculating the distance between any two feature points in the facial image feature points information, angle information; based on the distance information and angle information between any two feature points in the plurality of feature points, determine the first probability distribution of the grayscale image corresponding to each pres...
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