Emotion detection and classification: Using computer vision and deep learning, systems can analyze facial expressions to detect emotions. Facial landmarks (like the movement of eyebrows, lips, and eyes) are key features used to infer emotions such as happiness, sadness, anger, neutral, and disgust.
Image Processing involves the manipulation and analysis of digital images using various algorithms and techniques. It is widely used in fields such as computer vision, medical imaging, and machine learning. Common tasks include image enhancement, noise reduction, segmentation, and feature extraction. By transforming pixel data into meaningful information, image processing enables applications such as facial recognition, object detection, and autonomous driving. This project leverages cutting-edge tools and methodologies to perform advanced image processing tasks, demonstrating practical implementations and providing a foundation for further development.
Works and topics used: 1. Edge detection methods ---> Canny Edge Detection, 2. Confusion matrix, 3. Haarcascade Separation of images or Segregation -- Region (mouth, eye, eyebrows).
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#image_processing #digital signal #OpenCV