In recent years, enabling computer systems to recognize facial expressions and infer emotions from them in real time has become very important since such information can be used in emerging applications such as video ...
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ISBN:
(纸本)9781467391016
In recent years, enabling computer systems to recognize facial expressions and infer emotions from them in real time has become very important since such information can be used in emerging applications such as video games, educational software, computer-based tutoring for special need children for better human computer interactions. However, real time emotion recognition using video streams face challenges due to the varying illuminations. In this paper, we present a real time emotion recognition scheme using dense optical flow based approach and SVM classifier. Via extensive analysis using newly collected datasets of 370 videos, we demonstrate that our approach demonstrates high accuracy in recognizing 4 basic emotions: happy, angry, surprise and sad.
Video Surveillance has become an issue of utmost importance due to rising crime and violence rate in the world. Many algorithms exist to analyze the behavior of the moving objects. In this paper, an approach to detect...
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ISBN:
(纸本)9781538609262
Video Surveillance has become an issue of utmost importance due to rising crime and violence rate in the world. Many algorithms exist to analyze the behavior of the moving objects. In this paper, an approach to detect any violent behavior in videos using optical flow algorithms. The SDHA 2000 dataset is used to analyze foreground behavior. The main task in violence detection is to detect moving objects and classify their behavior using mean of intensity as violent or non-violent.
In recent years, enabling computer systems to recognize facial expressions and infer emotions from them in real time has become very important since such information can be used in emerging applications such as video ...
详细信息
ISBN:
(纸本)9781467391023
In recent years, enabling computer systems to recognize facial expressions and infer emotions from them in real time has become very important since such information can be used in emerging applications such as video games, educational software, computer-based tutoring for special need children for better human computer interactions. However, real time emotion recognition using video streams face challenges due to the varying illuminations. In this paper, we present a real time emotion recognition scheme using dense optical flow based approach and SVM classifier. Via extensive analysis using newly collected datasets of 370 videos, we demonstrate that our approach demonstrates high accuracy in recognizing 4 basic emotions: happy, angry, surprise and sad.
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