Face Feature Localization Using Gray Projection - MATLAB Implementation

This project implements a face feature localization algorithm using MATLAB programming language based on gray projection methodology. Face feature localization is a technique that analyzes facial images to determine the positions of key facial landmarks. Through the gray projection approach, we first convert the input face image into a grayscale representation, then utilize pixel intensity calculations to locate critical facial features such as eyes, nose, and mouth. The implementation involves several key MATLAB functions: imread() for image loading, rgb2gray() for color-to-grayscale conversion, and custom projection functions for horizontal and vertical intensity profiling. The algorithm works by computing intensity projections along both axes - horizontal projection helps locate features like mouth and eyebrows through row-wise intensity sums, while vertical projection identifies features like eyes and nose via column-wise intensity analysis. This approach enables precise localization and identification of key facial features in images, providing fundamental support for applications such as facial recognition systems, expression analysis, and biometric authentication. The project explores both theoretical principles and practical implementation of gray projection algorithms, with MATLAB code demonstrating feature extraction through peak detection in projection profiles and coordinate mapping techniques. By completing this project, developers will gain deeper understanding of facial feature localization algorithms while enhancing their programming skills in image processing and computer vision applications. The implementation includes error handling for varying illumination conditions and face orientations, making it robust for practical deployment.