Facial Recognition Seminar Report

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In this paper we introduce a new approach for facial expression recognition and emotional state recognition for human. 2D features were used in the existing system whereas the 3D features are used in the proposed system and dynamic analysis for natural interaction. In this survey automatic recognition is done through video sequence. The image processing is done by detecting the facial regions and 26 fiducial points are calculated which is taken as input frames. Based on the fiducial points facial expressions are recognized. Elastic Body Spline (EBS) is used for emotion classification with the feature extraction which depends on the 3Dmodel. This extracts the feature from realistic emotion expression and it is also applied in Driver’s Drowsiness Detection, Human Computer Interface, Psychological studies in Robotics which is automatically recognized through video sequence. The emotions are recognized from the fiducial points. Those emotions are taken as the input frame for human machine
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The expressions used to convey fear, anger, sadness, and happiness are similar throughout the world. A facial expression is one or more motions of the muscles beneath the skin of the face. These movements convey the emotional state of an individual to observers. Facial expressions are a form of nonverbal communication. It is accurate and it requires no physical interaction on behalf of the users. Humans facial expression either voluntarily or involuntarily produced. The neural mechanisms are responsible for controlling the expression which differs from case to case. Voluntary facial expressions are socially conditioned and followed by cortical route in the brain whereas involuntary facial expressions are innate and they are sub cortical route in the brain. Emotion plays a vital role in human-to-human interaction, allowing people

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