Combining Fingerprints and their Radon Transform as Input to Deep Learning for a Fingerprint Classification Task
Dhekra El Hamdi, Inès Elouedi, Abir Fathallah, Mai K. Nguyuen, Atef Hamouda · 2018
Fingerprint classification is a successful technique to reduce the search space of a large fingerprint database in identification systems. It is based on determining the class of a query fingerprint. In this work, we investigate the use of Conic Radon Transform (CRT) as a feature extractor and a deep learning technique in order to solve fingerprint classification tasks. The proposed approach is based on the CRT. The latter represents an extension of classical Radon Transform (RT) to integrate an image function f(x, y) over conic sections. The Radon technique enables the extraction of fingerprint's global characteristics which are invariant to geometrical transformations such as translation and rotation. We perform CRT on a source image, then we combine the Radon result with an original image and use them as an input for convolutional neural networks (CNN). In our experiments, our approach has yielded better accuracy than state-of-the-art methods based on explicit feature extraction, even others CNN methods based on original images.