Implementation of Snake Algorithm in MATLAB for Eye and Mouth Detection - Original Method

In this article, I will demonstrate how to implement eye and mouth detection using the snake algorithm in MATLAB. The original implementation method of this algorithm is both fascinating and practical. Through MATLAB's snake algorithm implementation, we can achieve accurate detection of eyes and mouths in images, providing significant convenience for research in image processing and computer vision fields. The implementation process involves several key steps: initializing active contours using MATLAB's contour function, setting energy minimization parameters through gradient vector flow, and iteratively deforming the snake model toward feature boundaries. The algorithm utilizes image gradient information and internal energy constraints to maintain contour smoothness while attracting the snake toward strong edges. Although relatively straightforward to implement, this method produces impressive results through proper parameter tuning and initialization strategies. In this article, I will provide detailed implementation steps for MATLAB's snake algorithm, accompanied by sample code snippets demonstrating contour initialization, energy function configuration, and iterative deformation processes. Additionally, I will include example images to help readers better understand the algorithm's working mechanism, particularly focusing on how the snake model converges around ocular and oral features using edge detection and energy minimization techniques. This content aims to assist researchers in image processing and computer vision fields by providing practical implementation insights and optimization approaches for feature detection applications.