Infant gesture detection algorithm Based on StarNet Multi-channel Cartesian product network

Infants communicate with others through nonverbal gestures in their early years, expressing their needs and emotions through gestural signs. But it is very challenging for adults to understand babies who have no language skills. In order to better understand these infant gestures, the author first constructed a dataset (babyhand_gesture), which contains four gestures, such as: "happy", "nervous", "sleep" and "sleepy". On this basis, this paper proposes the S-MCCP (Based on StarNet Multi-channel Cartesian product) network framework for infant gesture detection. This method not only utilizes the model expression capability of the high-dimensional feature space of the StarNet structure, but also utilizes the Cartesian product operation to enhance the number of feature images, thereby improving the model accuracy and generalization ability. Experimental results show that the algorithm successfully achieved a recognition accuracy of 96.4% on the dataset, which is nearly one percentage point higher than other algorithms. When tested on three other datasets, the proposed algorithm also had the highest accuracy.