Artificial Intelligence Framework for Universal Quantification of Surficial Anti‐Fingerprint Performance

Fingerprint resistance in consumer products is gaining prominence, necessitating reliable anti‐fingerprint quantification methods. Traditional approaches include contact angle measurements and image‐based methods. However, these often fail under realistic conditions due to transparency and complex textures, limiting accurate fingerprint resistance quantification. Herein, we propose an AI‐based framework inspired by human vision that distinguishes fingerprints from complex backgrounds and ranks their visibility from a single photographic image. AW‐Net, a novel segmentation tool, achieves 99% cluster accuracy and 93% pixel accuracy on glass surfaces. We introduce a grayscale fingerprint grading metric with five ordinal visibility levels, enabling interpretable, scalable fingerprint resistance quantification closely aligned with human visual perception. The framework establishes its univers…