Sigit Januarto’s scientific contributions

Facial recognition technology has rapidly advanced, but identifying individuals wearing glasses remains challenging due to altered or obscured facial features. This study addresses this issue by combining the Nearest Neighbor Interpolation Method and Naive Bayes Classification for bespectacled face identification. The method applies interpolation to enhance facial image quality, preserving critical features before classification by Naive Bayes into spectacle and non-spectacle classes. Using the Kaggle MeGlass dataset for training and testing, the approach achieved a training accuracy of 78%, a testing accuracy of 76%, and a cross-validation value of 0.70. These results indicate a significant improvement in recognizing bespectacled faces, contributing to enhanced accuracy in facial recognition systems. Despite these advancements, further improvements are possible, such as integrating more advanced models and expanding the dataset, which could lead to even greater accuracy and reliability in practical applications. This research provides a novel solution to a persistent challenge in facial recognition technology