JCAI

This research paper investigates an innovative approach to face detection by combining structural coordinates analysis with machine learning techniques. The study introduces a method wherein facial landmarks’ structural coordinates are utilized as crucial input features for training a machinelearning model. Integrating these coordinates enhances the model’s ability to discern intricate facial features, thereby improving the precision of face detection. The paper analyzes the model’s performance, evaluating key metrics such as accuracy, precision, recall, and F1 Score. The results demonstrate notable success, with accuracy exceeding 90%, affirming the effectiveness of the proposed methodology. Additionally, the paper provides insights from a confusion matrix, offering a nuanced understanding of the model’s ability to classify positive and negative instances correctly. Sample image predictions further illustrate the practical implications of the proposed approach, showcasing instances of both accurate and challenging detections. This research contributes to the advancement of face detection technology and opens avenues for broader applications, ranging from biometric security systems to interactive technologies reliant on facial recognition. The synergistic integration of structural coordinates and machine learning in face detection signifies a promising avenue for future computer vision and biometrics developments.
Keywords : Structural coordinates analysis; machine learning model; computer vision; biometrics;face detection.
Citation : Y. Liu and J. Zhang “Face Detection with Structural Coordinates for the Estimation of Patterns Using Machine Learning Model”, Journal of Computer Allied Intelligence (JCAI), vol.01, no.01, pp.41-53,2023.