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Users can be able to leverage\ntheir webcam to decode what their body language says at a point in time. So, specifically in\norder to do this, the detector is leveraging pre-trained data as well as a custom machine learning model\nto be able to take the landmarks from user's face as well as different poses from their body.\u003c/p\u003e\n\n\u003chr/\u003e\n\n## Outline of Methodology \n\n![Working_Model](Images/Working_Model.png)\n\n\u003chr/\u003e\n\n## Dataset\n\n\u003cp\u003eOpenCV and CSV are used to capture the landmarks related to classified facial and\ngesturing expressions. The collected data are stored in the spreadsheet which are used to train the\nmachine to assume the real time detected poses. The more we collect landmarks for different\nbody and face gestures, the more we get the accurate prediction results.\u003c/p\u003e\n\n\u003chr/\u003e\n\n## Machine Learning Models\n\nI have used the following 4 machine learning models for expression detection.\n\n* Logistic Regression\n* Ridge Classifier\n* Random Forest\n* Gradient Boosting\n\n### Comparisons Between Models\n\n![Happiness_Detection](Images/Happiness_Detection.png)\n\n![Sad_Detection](Images/Sad_Detection.png)\n\n![Victory_Detection](Images/Victory_Detection.png)\n\n\u003c/hr\u003e\n\n## Results\n\n![Results](Images/Results.png)\n\n## Required Resources\n\n* Language: Python\n* Platform: Jupyter Notebook IDE\n* Packages: MediaPipe, cv2, csv, os, numpy, pandas, sklearn, train_test_split, pickle\n\n\u003chr/\u003e\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahasannn%2Freal-time-expression-detector","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahasannn%2Freal-time-expression-detector","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahasannn%2Freal-time-expression-detector/lists"}