Utilizing Fingerphotos with Deep Learning Techniques to Recognize Individuals
DOI:
https://doi.org/10.56286/ntujet.v2i1.318Keywords:
Fingerphoto, Verification, Deep Learning, Recognition, Convolutional Neural Networks (CNN).Abstract
Biometrics based personal verification for mobile phone devices are currently well-known. In this study, a verification approach is suggested depending on fingerphoto pictures. Couple of Deep Fingerphotos Learning (CDFL) approach is proposed, where two Deep Learning (DL) networks are involved. A fingerphoto picture of the index finger is verified using the first DL network. To recognize a fingerphoto picture of a middle finger, another DL network is used. Then, the outputs of the two networks are integrated. Fingerphoto pictures from the IIITD smartphone fingerphoto dataset are used in this work. The results yield that the accuracy of the first DL network is reported as 76.95% and the accuracy of the second DL network is reported as 86.33%. Whereas, the overall accuracy of the proposed CDFL method after integrating both DL networks is benchmarked as 96.48%.
