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 universal applicability for fingerprint resistance quantification across diverse material classes, with 97% cluster accuracy and 90% pixel accuracy on complex and matte wood surfaces, underscoring that its high performance extends beyond relatively simple and glossy glass to more challenging substrates. Overall, by integrating composite visual cues through data augmentation and structured modeling, the framework delivers robust and reliable fingerprint resistance quantification, resolving visibility into five distinct levels across consumer product surfaces.