M. Bishay, Kenneth Preston, Matthew Strafuss, Graham Page, Jay Turcot, Mohammad Mavadati
{"title":"AFFDEX 2.0:一个实时面部表情分析工具包","authors":"M. Bishay, Kenneth Preston, Matthew Strafuss, Graham Page, Jay Turcot, Mohammad Mavadati","doi":"10.1109/FG57933.2023.10042673","DOIUrl":null,"url":null,"abstract":"In this paper we introduce AFFDEX 2.0 – a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [36], that is capable of tracking faces at challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 outperforms the state-of-the-art methods in AU detection and emotion recognition. AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.","PeriodicalId":318766,"journal":{"name":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit\",\"authors\":\"M. Bishay, Kenneth Preston, Matthew Strafuss, Graham Page, Jay Turcot, Mohammad Mavadati\",\"doi\":\"10.1109/FG57933.2023.10042673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce AFFDEX 2.0 – a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [36], that is capable of tracking faces at challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 outperforms the state-of-the-art methods in AU detection and emotion recognition. AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.\",\"PeriodicalId\":318766,\"journal\":{\"name\":\"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FG57933.2023.10042673\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FG57933.2023.10042673","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit
In this paper we introduce AFFDEX 2.0 – a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [36], that is capable of tracking faces at challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 outperforms the state-of-the-art methods in AU detection and emotion recognition. AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.