公开(公告)号：US11967175B2

公开(公告)日：2024-04-23

申请号：US18322517

申请日：2023-05-23

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Sannyuya Liu ,  Zongkai Yang ,  Xiaoliang Zhu ,  Zhicheng Dai ,  Liang Zhao

IPC: G06K9/00 ,  G06V10/24 ,  G06V10/62 ,  G06V10/77 ,  G06V10/80 ,  G06V10/82 ,  G06V20/40 ,  G06V40/16 ,  G06V10/774

CPC classification number: G06V40/165 ,  G06V10/247 ,  G06V10/62 ,  G06V10/7715 ,  G06V10/806 ,  G06V10/82 ,  G06V20/41 ,  G06V40/171 ,  G06V40/174 ,  G06V10/774

Abstract: Provided are a facial expression recognition method and system combined with an attention mechanism. The method comprises: detecting faces comprised in each video frame in a video sequence, and extracting corresponding facial ROIs, so as to obtain facial pictures in each video frame; aligning the facial pictures in each video frame on the basis of location information of facial feature points of the facial pictures; inputting the aligned facial pictures into a residual neural network, and extracting spatial features of facial expressions corresponding to the facial pictures; inputting the spatial features of the facial expressions into a hybrid attention module to acquire fused features of the facial expressions; inputting the fused features of the facial expressions into a gated recurrent unit, and extracting temporal features of the facial expressions; and inputting the temporal features of the facial expressions into a fully connected layer, and classifying and recognizing the facial expressions.

公开(公告)号：US12254692B2

公开(公告)日：2025-03-18

申请号：US18011847

申请日：2021-09-07

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Sannyuya Liu ,  Zengzhao Chen ,  Zhicheng Dai ,  Shengming Wang ,  Xiuling He ,  Baolin Yi

IPC: G06V20/40 ,  G06Q50/20 ,  G06V10/774 ,  G06V40/20 ,  G10L25/57 ,  G10L25/78

Abstract: The present invention discloses construction method and system of a descriptive model of classroom teaching behavior events. The construction method includes steps as the followings: acquiring classroom teaching video data to be trained; dividing the classroom teaching video data to be trained into multiple events according to utterances of a teacher by using a voice activity detection technology; and performing multi-modal recognition on all events by using multiple artificial intelligence technologies to divide the events into sub-events in multiple dimensions, establishing an event descriptive model according to the sub-events, and describing various teaching behavior events of the teacher in a classroom. The present invention divides a classroom video according to voice, which can ensure the completeness of the teacher's non-verbal behavior in each event to the greatest extent. Also, a descriptive model that uniformly describes all events is established by extracting commonality between different events, which can not only complete the description of various teaching behaviors of the teacher, but also reflect the correlation between events, so that the events are no longer isolated.

公开(公告)号：US12036021B2

公开(公告)日：2024-07-16

申请号：US18511919

申请日：2023-11-16

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Liang Zhao ,  Sannyuya Liu ,  Zongkai Yang ,  Xiaoliang Zhu ,  Jianwen Sun ,  Qing Li ,  Zhicheng Dai

IPC: A61B8/14 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/1171 ,  A61B5/16 ,  G06N3/0464 ,  G06N3/08 ,  G06V10/30 ,  G06V40/16 ,  A61B5/024 ,  A61B5/08

CPC classification number: A61B5/16 ,  A61B5/0205 ,  A61B5/1176 ,  A61B5/725 ,  A61B5/726 ,  A61B5/7264 ,  G06N3/0464 ,  G06N3/08 ,  G06V10/30 ,  G06V40/161 ,  A61B5/02427 ,  A61B5/0816 ,  G06V2201/03

Abstract: The present disclosure provides a non-contact fatigue detection system and method based on rPPG. The system and method adopt multi-thread synchronous communication for real-time acquisition and processing of rPPG signal, enabling fatigue status detection. In this setup, the first thread handles real-time rPPG data capture, storage and concatenation, while the second thread conducts real-time analysis and fatigue detection of rPPG data. Through a combination of skin detection and LUV color space conversion, rPPG raw signal extraction is achieved, effectively eliminating interference from internal and external environmental facial noise; Subsequently, an adaptive multi-stage filtering process enhances the signal-to-noise ratio, and a multi-dimensional fusion CNN model ensures accurate detection of respiration and heart rate. The final step involves multi-channel data fusion of respiration and heartbeats, succeeding in not only learning person-independent features for fatigue detection but also detecting early fatigue with very high accuracy.

公开(公告)号：US11568012B2

公开(公告)日：2023-01-31

申请号：US16605806

申请日：2018-05-08

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Zongkai Yang ,  Sannyuya Liu ,  Dongbo Zhou ,  Jianwen Sun ,  Jiangbo Shu ,  Hao Li

IPC: G06F16/00 ,  G06F16/9537 ,  G06F16/9535 ,  G09B5/12

Abstract: The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.