Image-Based Identification of Ophthalmic Lens Optical Characteristics: A Benchmark of Local Texture Descriptors for Non-Destructive Optical Inspection

Despite recent advances in computer vision and optical imaging, the image-based recognition of ophthalmic lens categories characterized by different refractive indices and anti-reflective coating types remains largely unexplored. In this work, we formulate this problem as a non-destructive optical inspection task, where lens-induced visual changes are treated as subtle micro-texture signatures produced by the interaction between the lens and a controlled visual target. Although these signatures are often imperceptible to the human eye, they can be quantified using local texture analysis. To establish the first systematic benchmark for this emerging application, we present a comprehensive evaluation of state-of-the-art local texture descriptors, with particular emphasis on Local Binary Pattern (LBP)-like methods, under unified evaluation protocols. The evaluation is conducted on two dedicated ophthalmic lens image datasets acquired under controlled conditions, using consistent visual targets designed to reveal lens-induced texture and color variations. This setting enables a rigorous assessment of whether handcrafted micro-texture descriptors can capture discriminative image signatures associated with different refractive indices and anti-reflective coating types. The experimental results demonstrate that several modern LBP variants provide excellent recognition performance, confirming the relevance of local micro-texture analysis for image-based ophthalmic lens category recognition. Comparisons with pretrained deep feature extractors further show that although deep representations generally achieve the highest recognition rates, several handcrafted descriptors offer comparable performance while requiring neither network training nor fine-tuning. Beyond recognition accuracy, the benchmark investigates performance stability across datasets, robustness under limited training samples, classifier influence, and the statistical significance of the observed performance differences. Overall, this work establishes the first reproducible image-based benchmark for non-destructive ophthalmic lens inspection and provides practical guidelines for selecting local texture descriptors in ophthalmic lens recognition systems.