<![CDATA[Facial Wrinkles are one of the key indicators in identifying signs of aging on the skin. Detecting facial wrinkles poses a challenge in image processing due to their complex, coarse texture, which is often difficult for computers to recognize, especially under varying lighting conditions, camera angles, and facial expressions. This study focuses on the application of features using Gabor Filters for the texture feature extraction, with the final results determined by the Naïve Bayes classification algoritm. In this study, 200 facial images were used, divided into two clases, with 100 images per class serving as training data. For the test data, 100 facial images were used, consisting of 50 wrinkled facial images and 50 non-wrinkled facial images. Based on the test result using the Confusion Matrix, the accuracy was 74%, precision 80%, recall 64% and F1-Score 71%. These results indicate that the combination of Gabor filters and Naïve Bayes is quite effective in recognizing wrinkle patterns on the face based on extracted texture feature, and can serve as a faoundation for developing more accurate facial wrinkle detection systems in the future.]]>
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