The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement.