| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_cb351a5a4e03ee3773bd0df5dbae9ac3 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30201
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-248
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-269
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-269
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-246
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate |
2021-07-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_afa00768a510f5a0c278e3ef88f8e5da
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_61c08ae99a0472be068f10517b1a2da1
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d7e7475c135eeeffc77fd5614fce3295 |
| publicationDate |
2021-10-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113537008-A |
| titleOfInvention |
Micro-expression recognition method based on adaptive motion amplification and convolutional neural network |
| abstract |
The invention discloses a micro-expression recognition method based on self-adaptive motion amplification and convolutional neural network. The starting frame of the image sequence, and use the vertex frame positioning algorithm to calculate the vertex frame picture; Step 3: Use the adaptive motion magnification method to determine the appropriate magnification, and perform motion magnification on the vertex frame according to the determined magnification to enhance the microscopic image. Expression features; Step 4: Obtain the optical flow characteristics of the micro-expression video according to the starting frame and the enlarged vertex frame, and obtain the horizontal optical flow, vertical optical flow and optical strain; Step 5: Establish a convolution for micro-expression recognition Neural network model, and use this model to perform migration learning from macro-expression to micro-expression; Step 6, input the optical flow feature into the model after migration learning, and output the time-space feature, after the model is trained to realize micro-expression recognition. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115797335-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115359534-A |
| priorityDate |
2021-07-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |