People identification via tongue print using fine-tuning deep learning

Abstract

Many person-verification systems are critical in security systems for verifying passage through doors opened to specific people using various techniques. People can use electronic payment methods and security apps to generate codes for quick, remote financial transactions. Older systems required precision and speed. Many alternative methods were developed by technology and artificial intelligence to make such operations simple and quick. The identification of tongue prints is discussed in this paper. Tongue prints, like fingerprints, are unique to each individual. The tongue was used in this study because it is unique among such organs. The tongue is protected by the lips. This guards against taking a tongue print by force. Some people distort their fingerprints, making fingerprint recognition systems unable to recognize them. Car accidents cause facial distortion, which distorts the system and prevents it from distinguishing facial prints, so the tongue was used as a fingerprint in this study. A database of 1,104 images for 138 Mustansiriyah University College of Science students yielded an average of eight images per individual. VGG16 was implemented for transfer learning and fine-tuning. In comparison to previous studies, the accuracy achieved was more than 91%.