Face Recognition through Landmarks to Find Accurate Distance Between Two Landmarks Using Pythagorean Algorithm

Being accurate and efficient and compact is crucial for a face recognition system for practical use. This technique relies on the fact that these features are relatively stable and unique for each individual, making them ideal for face recognition purposes. Face landmark detection involves identifying and localizing key points or landmarks on a person's face. These landmarks are typically areas such as the corners of the eyes and mouth, the tip of the nose, and the contour of the jawline. More concretely, we customize an end-to-end method to calculate the distances between two or more landmarks. The process of face recognition through landmarks involves various steps, including face detection, landmark detection, feature extraction, and matching. Feature extraction involves measuring the distance and angles between these landmarks to create a unique facial signature for each individual. This approach is independent of any face detection and landmark detection algorithms where the distances are to be calculated between the 468 3D landmarks using the proposed technique. The entire concept and the problems for each technique are highlighted in this paper