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Real time emotion recognition, using OpenCV and haarcascade algorithm for face detection from the video source, then I've done emotion recognition using a model trained on FER-2013 dataset with Tensorflow. and also as an other solution, I used DeepFace package for emotion recognition as a prefabricated solution.
The objective is to detect Moving Cars in a video file using OpenCV using the HaarCascade_car.xml file and then, you will use OpenCV to detect the License plates of a Car using the HaarCascade_russian_plate_numberXMLfile
This folder contains the code for the Face recognition model that I implemented without using the facerecognition library. Harcascade classifier was used. Project is at basic level and was aimed for learning.
This project leverages the power of computer vision and advanced image processing techniques to accurately detect and measure the speed of vehicles in real-time. The combination of Python programming language, OpenCV for computer vision tasks, and Dlib's Haar Cascade method ensures robust and efficient speed detection.
The Facial Recognition and Detection Application provides both image and live camera facial recognition and detection. In image mode, it identifies faces, eyes, and smiles within loaded images. In live camera mode, it continuously captures real-time video and performs facial recognition, eye detection, and smile detection,