Developing an algorithm for Fingerprint Recognition of Newborns and Toddlers based on Deep Neural Networks and Transfer Learning

Abstract

In the late 19th century, Sir Galton started the use of fingerprint recognition as a method for baby identification. However, even after the extent two centuries, fingerprint identification for newborns and toddlers has not advanced to the same level as that for adults. According to research from the International Centre for Missing and Exploited Children, there is a growing request for fingerprint identification of babies because more than a million of them go missing each year. So, we want to ensure that newborns and toddlers have access to their rights, including vaccinations, healthcare, and nutritional supplements until they reach school age. This paper develops an algorithm that has been used in previous research to recognize newborns and toddlers to produce remarkable and promising results by Pre-trained model deep neural networks and transfer learning to extract the deep features and to get the identification score, aiming to enhance accuracy and validate. The proposed model is validated on the fingerprint dataset of babies named as NITG dataset. The fingerprint recognition model proposed in the research showcases excellent results, where achieved 100% accuracy in training and 90.35% in validation.