LivDet in Action - Fingerprint Liveness Detection Competition 2019

The International Fingerprint liveness Detection Competition (LivDet) is an open and well-acknowledged meeting point of academies and private companies that deal with the problem of distinguishing images coming from reproductions of fingerprints made of artificial materials and images relative to real fingerprints. In this edition of LivDet we invited the competitors to propose integrated algorithms with matching systems. The goal was to investigate at which extent this integration impact on the whole performance. Twelve algorithms were submitted to the competition, eight of which worked on integrated systems.Comment: Preprint version of a paper accepted at ICB 201

Mobile Computing in Past, Present and Future

Mobile Computing defines that a device which permits the flow of transmission of data from one computer to another by never been connected to the Physical link layers. Mobile voice communications which is in demands all over the world is having a great increment of the user subscribers to many networks protocols from last two to three years. This concept is normally called as the Principle of the mobile computing. This has become very interesting in the growth of the technology which allows the users to transmit the information details of data. The protection attributes of the mobile computing are User Authentication which corrects the identity of the user which has been subscribed to this service. User anonymity which is the international mobile subscriber identity abbreviated as the IMSI which is normally used to the networks to properly use for the identification for the user subscribers. Fraud Prevention is for the prevention of hackers who attack the sites. Protection of user data prevents the data of user which is used to protect the saved information of the end users. Applications related to this device are for the Estate agents to work on home as well as on the construction sites too. Emergency time to inform the others about the emergency condition that has taken place. In justice courts to take a proper straight decisions against the criminals. In industries for the directors to work on computers using a mobile system. Stock related issues for new latest updates of the shares going on. Card verification to verify the card in banks and other places too. These increments in the virtual technology, circuits and system speed the mobile computing in the future will be at the developed stage from today. The demand for the mobile computations will be on large scale in the coming future days and these devices will generate a bright flash in future

Enhancing child safety with accurate fingerprint identification using deep learning technology

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children

Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection

Machine learning experts expected that transfer learning will be the next research frontier. Indeed, in the era of deep learning and big data, there are many powerful pre-trained CNN models that have been deployed. Therefore, using the concept of transfer learning, these pre-trained CNN models could be re-trained to tackle a new pattern recognition problem. As such, this work is aiming to investigate the application of transferred VGG19-based CNN model to solve the problem of fingerprint liveness recognition. In particular, the transferred VGG19-based CNN model will be modified, re-trained, and finely tuned to recognize real and fake fingerprint images. Moreover, different architecture of the transferred VGG19-based CNN model has examined including shallow model, medium model, and deep model. To assess the performances of each architecture, LivDet2009 database was employed. Reported results indicated that the best recognition rate was achieved from shallow VGG19-based CNN model with 92% accuracy