Parameters Research of Facial Emotion Detection Algorithm Based on Machine Learning
The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze human psychological activities, so as to play a role in the fields of entertainment, education, intelligent medical treatment and so on. Differe...
Uloženo v:
| Vydáno v: | 2024 Asia Pacific Conference on Innovation in Technology (APCIT) s. 1 - 6 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
26.07.2024
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze human psychological activities, so as to play a role in the fields of entertainment, education, intelligent medical treatment and so on. Different emotion recognition algorithms and different parameters of the same algorithm have different recognition effects. Among them, muscle-based feature model and 68 feature point calibration are two common face emotion recognition methods. The former uses deep learning to judge the emotional state by analyzing the movement of face muscles, while the latter calculates various feature parameters through the position relationship of 68 key points of the face, and then judges the emotional state. This paper mainly discusses the calibration method of 68 feature points. Through the use of two machine learning algorithms (KNN, SVM) and the study of different parameters in the algorithm, the influence of different parameters on the emotion recognition effect is compared and analyzed. The experiment proves that by detecting 68 key points of face, we can find the optimal parameter value in the current classification task. |
|---|---|
| AbstractList | The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze human psychological activities, so as to play a role in the fields of entertainment, education, intelligent medical treatment and so on. Different emotion recognition algorithms and different parameters of the same algorithm have different recognition effects. Among them, muscle-based feature model and 68 feature point calibration are two common face emotion recognition methods. The former uses deep learning to judge the emotional state by analyzing the movement of face muscles, while the latter calculates various feature parameters through the position relationship of 68 key points of the face, and then judges the emotional state. This paper mainly discusses the calibration method of 68 feature points. Through the use of two machine learning algorithms (KNN, SVM) and the study of different parameters in the algorithm, the influence of different parameters on the emotion recognition effect is compared and analyzed. The experiment proves that by detecting 68 key points of face, we can find the optimal parameter value in the current classification task. |
| Author | Jiang, Shi Xiao Khan, Asif Ting, Zhou Yadav, Amit |
| Author_xml | – sequence: 1 givenname: Zhou surname: Ting fullname: Ting, Zhou email: zhouting@nsu.edu.cn organization: Chengdu Neusoft University,Department of Computer Science and Technology,Chengdu,China,611844 – sequence: 2 givenname: Amit surname: Yadav fullname: Yadav, Amit email: amitaryan2u@yahoo.com organization: Charles Darwin University,College of Engineering IT and Environment,Australia – sequence: 3 givenname: Shi Xiao surname: Jiang fullname: Jiang, Shi Xiao email: 2974914972@qq.com organization: Chengdu Neusoft University,Department of Computer Science and Technology,Chengdu,China,611844 – sequence: 4 givenname: Asif surname: Khan fullname: Khan, Asif email: asif05amu@yahoo.com organization: Integral University,Department of Computer Application,Lucknow,India |
| BookMark | eNo1T8lOwzAUNBIcoPQPOPgHErzE2zGEFioFUUHu1YuXxlIW5OTC3xMBPc2imZHmDl2P0-gRwpTklBLzWB6rQyMZISpnhBU5JVJxwcwV2hplNBeESyq4ukWfR0gw-MWnGX_42UOyHZ4C3oON0OPdMC1xGvHzmrC_rOzPU4pLN-AnmL3Dq_UGtoujx_XaHuN4vkc3AfrZb_9xg5r9rqles_r95VCVdRYNXTLDtJVcCeoohWBXAcIGxV0wmlimAgRTBEmsaa3mxBGtJXHGFW0rrWCSb9DD32z03p--UhwgfZ8uV_kPt55Oww |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/APCIT62007.2024.10673529 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) (UW System Shared) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology |
| EISBN | 9798350361537 |
| EndPage | 6 |
| ExternalDocumentID | 10673529 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i91t-928c63751d11afc8c6a5cf73df980c27faf94f60c9bc830d08860d9d4bb6c5263 |
| IEDL.DBID | RIE |
| IngestDate | Wed Sep 25 09:21:52 EDT 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i91t-928c63751d11afc8c6a5cf73df980c27faf94f60c9bc830d08860d9d4bb6c5263 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10673529 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-July-26 |
| PublicationDateYYYYMMDD | 2024-07-26 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-July-26 day: 26 |
| PublicationDecade | 2020 |
| PublicationTitle | 2024 Asia Pacific Conference on Innovation in Technology (APCIT) |
| PublicationTitleAbbrev | APCIT |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.877326 |
| Snippet | The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithm parameters Emotion recognition Face recognition Machine learning Machine learning algorithms Nearest neighbor methods Psychology Support vector machines Technological innovation |
| Title | Parameters Research of Facial Emotion Detection Algorithm Based on Machine Learning |
| URI | https://ieeexplore.ieee.org/document/10673529 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA62eOjJV8U3OXhNu9lsk51jrS0KWhbsobeSzaMWdLfUreC_N0m7FQ8evCVDIDADmUe--QahW2NdEJ9YIJpJRRJIEwJORmispISE0lwGntknMR6n0ylk22b10AtjjAngM9Pxy_CXr0u19qWyrqc7cwEDNFBDCL5p1qrRORF0-9ngccIDK6FL6ZNOffzX4JTgN0YH_7zxELV_OvBwtvMtR2jPFMeotXutvk7QSyY9rsqTY-IaPodLi0fSF8HxcDOeB9-bKoCtCtx_m5erRfX6ju-c59LYiZ4DktLgLcnqvI0mo-Fk8EC2ExLIAmhFIE4VZ6JHNaXSKreRPWUF0xbSSMXCSuvswCMFuUpZpN2LwiMNOslzrnoxZ6eoWZSFOUNYaA0udXFHmXAREk855VpTxkHFOYvgHLW9dmbLDQfGrFbMxR_yS9TyNvBV0JhfoWa1WptrtK8-q8XH6iZY7hs-X5sV |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4omsjJF8a3PXhd2G6X7vaICIEIZBP3wI10-0AS3TW4mPjvbcuC8eDBWztp02QmmWmn33wDcK-0ucSHmnmScOGFLA49ZmQeDgTnLMQ4445ndhRNJvF0ypKqWN3VwiilHPhMNe3Q_eXLQqxsqqxl6c7MhYHtwp5tnVWVa23wOT5rdZLuMKWOl9A86sPmZsOv1ikucvQP_3nmETR-avBQso0ux7Cj8hOob_3V1yk8J9wiqyw9JtoA6FChUZ_bNDjqrRv0oEdVOrhVjjqv82K5KF_e0IOJXRIZ0dhhKRWqaFbnDUj7vbQ78KoeCd6C4dJjQSwoidpYYsy1MBPeFjoiUrPYF0GkuTaWoL5gmYiJL41Pob5kMswyKtoBJWdQy4tcnQOKpGTm8WKWksgol8YUUykxoUwEGfHZBTSsdmbvaxaM2UYxl3_I7-BgkI5Hs9Fw8nQFdWsPmxMN6DXUyuVK3cC--CwXH8tbZ8VvgrqeXg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2024+Asia+Pacific+Conference+on+Innovation+in+Technology+%28APCIT%29&rft.atitle=Parameters+Research+of+Facial+Emotion+Detection+Algorithm+Based+on+Machine+Learning&rft.au=Ting%2C+Zhou&rft.au=Yadav%2C+Amit&rft.au=Jiang%2C+Shi+Xiao&rft.au=Khan%2C+Asif&rft.date=2024-07-26&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FAPCIT62007.2024.10673529&rft.externalDocID=10673529 |