This project is a method of recognition in real time that traces the human mood itself and to map out the human behaviour traits with their physiological features. Recognition of emotion is the phase of human emotion identification. In recognising the emotions of others, people vary widely in their precision. Usage of this technology is to help people with emotion recognition is a relatively nascent research area. In general, if several modalities are used in connection, the technology performs better. Most work has been performed to date to automate the identification of video facial expressions, audio spoken expressions, written text expressions, and physiology as measured by wearables. A real-time recognition framework was built in this project that traces the very mood of the individual using some of the libraries such as Keras, OpenCV, Tensor flow, SciPy, The Python Alternative To Matlab, etc. HOF and SVM were used to tackle the problem of recognition. At each point, optical flows calculate the rotation between two frames relative to an observer. For image recognition, the Deep Convolutional Neural Networks were used. It was concluded that the application of the proposed strategy was accurate and effective.
Emotions, Automatic Recognition, Facial expression, Keras and Open CV HOF and SVM, Optical Flow, DCCN
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