People Recognition via Tongue Print Using Deep and Machine Learning

The tongue is a unique organ that is well protected inside the mouth and not affected by external factors; it is also difficult to forge. Several biometric systems are widely used for authentication and recognition, such as fingerprints, faces, iris, sound, retina, etc. Traditional biometrics represent a challenge and an obstacle as they can be falsified, duplicates can be made (e.g., iris, face, fingers, signature), or they are expensive and rarely used (e.g., DNA). The increased security measures called for modern biometrics that is more secure, less expensive, and cannot be falsified. As a result, the goal of this paper is to create a system for distinguishing people based on their tongue prints. It will contribute to solving many forensic issues and increasing electronic security because it has features suitable for identification and biometrically distinguishing between people. In this paper, the tongue is located based on the fixed window size method. After tongue localization (ROI), feature extraction using the VGG-16 model, and a classification system that uses both transfer learning and machine learning as VGG-16, XGBoost, KNN, and RF classifiers, extracted features are then trained for personal identification. The dataset consisted of 1085 tongue images of 138 people with a test ratio of 20%, and the results achieved an accuracy of 92%. The process of distinguishing people through tongue prints has proven to be effective and accurate.