Automated human behavioral analysis framework using facial feature extraction and machine learning

机译：使用面部特征提取和机器学习的自动化人类行为分析框架

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Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1% of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.

机译：情绪智力对于理解和预测人类行为至关重要。尽管使用非侵入性方法可以最好地捕捉人的情感，但是由于诸如系统复杂性，计算时间和决策响应时间之类的因素，自动行为分析的现实受到了阻碍。在本文中，我们提出了一个能够识别个人情绪以识别任何可疑行为的框架。我们的研究表明，使用面部特征提取和机器学习技术的合作者，情感分类的准确率达到91.1％，从而胜过现有的最新方法。

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《Asilomar Conference on Signals, Systems Computers》|2013年|911-914|共4页
会议地点 Pacific Grove CA(US)
作者
Smirnov Demiyan; Banger Sean; Davis Sara; Muraleedharan Rajani;
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Department of Electrical and Computer Engineering Rowan University Glassboro NJ- 08028c;

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2. Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm [J] . Satoru Hiwa, Shogo Obuchi, Tomoyuki Hiroyasu Computational intelligence and neuroscience . 2018,第3期

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4. Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm [J] . Youngjun Kim, Uchechukuwu David, Yeonsik Noh Innovation in aging. . 2020,第Supplementa1期

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5. Automated human behavioral analysis framework using facial feature extraction and machine learning [C] . Smirnov Demiyan, Banger Sean, Davis Sara, Asilomar Conference on Signals, Systems Computers . 2013

机译：使用面部特征提取和机器学习自动化人行为分析框架
6. Feature Extraction Using Topological Data Analysis for Machine Learning and Network Science Applications [D] . Guo, Wei . 2020

机译：采用机器学习和网络科学应用的拓扑数据分析特征提取
7. Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm [O] . Satoru Hiwa, Shogo Obuchi, Tomoyuki Hiroyasu 2018

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8. 3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques. [O] . Al-Qatawneh Sokyna M.S. 2010

机译：3D面部特征提取和识别。 3D人脸识别研究：人脸数据的校正和规范化，人脸特征的提取以及使用机器学习技术的分类。

Automated human behavioral analysis framework using facial feature extraction and machine learning

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