Ensuring a high level of adherence to wearing spectacles is essential to preserve eye health and achieve optimal vision correction. Comprehending the factors influencing adherence to wearing spectacles can inform strategies to improve eye care outcomes. This study aimed to assess the prevalence and factors influencing adherence to wearing spectacles among Moroccan adults residing the Beni-Mellal Khenifra region. A cross-sectional interview survey was conducted involving 389 adult spectacle wearers. Participants were recruited through a multilevel random sampling technique and interviewed using a structured questionnaire. This questionnaire collected data on demographics, spectacle prescription, usage patterns, and barriers to compliance. Compliance was assessed based on self-reported frequency and duration of spectacle wear according to the prescription. Possible factors influencing the wearing of spectacles were investigated using both univariate and multivariate regression models. This study revealed that 57.6% of participants adhered to the recommended use of spectacles. Factors associated with compliance were higher education level, longer duration of spectacle use, awareness about the importance of spectacle wear, absence of a family history, specific refractive error types, and increased severity of refractive errors. Participants reported various reasons for not adhering to the recommended use of spectacles, with the most common being forgetfulness, perceiving improved vision without spectacle, discomfort while wearing spectacle, difficulty adapting to spectacle, and loss of spectacle. Given the high prevalence of non-compliance to spectacle wear among adults in the Beni-Mellal Khenifra region, it is evident that additional efforts are required to improve understanding and education regarding the advantages of consistent spectacle usage. Targeted educational and awareness initiatives have the potential to substantially enhance adherence rates and consequently improve visual health outcomes in the region.

Despite substantial progress in optical imaging and computer vision, the image-based identification of ophthalmic lens characteristics, such as refractive index and anti-reflective coating type, remains largely unexplored. This work introduces 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 a first experimental reference for this emerging application, we present a systematic benchmark 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 encode latent optical properties related to refractive index and coating type. The experimental results show that several modern LBP variants achieve competitive recognition performance, confirming the relevance of local micro-texture analysis for image-based ophthalmic lens characteristic identification. Beyond accuracy, the study analyzes dataset dependence, the effect of training-set size, performance stability, and the influence of enriched local encoding strategies and neighborhood sampling schemes. Overall, this work introduces an image-based benchmark framework for non-destructive ophthalmic lens inspection and provides practical guidelines for selecting local texture descriptors in lens characteristic identification systems.

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.