This paper presents an integrated system for emotion detection using facial detection and recognition. we have taken into account the fact that emotions are most widely represented with eyes and mouth expressions. In this research effort, we implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system that accomplishes the tasks of face detection, emotion classification, and generating the content according to the emotion or mood of the person simultaneously in one blended step using our proposed CNN architecture. Our proposed model consisted of modules such as image processing, Feature extraction, feature classification, and recommendation process. The images used in the experiment are pre-processed with various image processing methods like canny edge detection, histogram equalization, fit ellipse, and FER dataset is mediated for conducting the experiments. With a trained profile that can be updated flexibly, a user can detect his/her behavior on a real-time basis. It utilizes the state of the art of face detection and recognition algorithms.
classification and recognition, Convolutional Neural Networks, Feature Extraction
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