facial landmarks and why they are used in computer vision applications. From there, I’ll demonstrate how to detect and extract the facial landmarks using dlib library, OpenCV, and Python. Finally, we’ll look at some results of applying facial landmark detection to images.
Price : 6000
.svg)
Face landmark recognition is a crucial area in the field of computer vision and image processing, widely used in applications such as facial expression analysis, identity verification, emotion detection, and augmented reality. Facial landmarks are key points on a human face—such as the corners of the eyes, the tip of the nose, the edges of the lips, and the outline of the jaw—that define the structure and orientation of the face.
This project, Face Landmarks Recognition using Image Processing, aims to detect and accurately identify these facial key points from an input image or video stream using advanced image processing and machine learning techniques. By leveraging libraries such as OpenCV and Dlib in Python, the system locates facial features and overlays them in real-time, enabling further analysis or interaction.
The main goal of the system is to provide an efficient and accurate method for recognizing facial landmarks, which can be used in various domains including security, healthcare, entertainment, and human-computer interaction. This project highlights the growing potential of combining image processing with intelligent algorithms to interpret and understand human facial features automatically.