Please use this identifier to cite or link to this item:
https://dspace.iiti.ac.in/handle/123456789/5188
Title: | Deep Convolutional Neural Network for Dot and Incipient Ridge Detection in High-resolution Fingerprints |
Authors: | Anand, Vijay Kanhangad, Vivek |
Keywords: | Convolution;Network security;Neural networks;Palmprint recognition;Convolutional neural network;Fingerprint images;Fingerprint Recognition;High resolution;Latent fingerprint;Level-1;Post processing;Ridge detections;Deep neural networks |
Issue Date: | 2019 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Citation: | Anand, V., & Kanhangad, V. (2019). Deep convolutional neural network for dot and incipient ridge detection in high-resolution fingerprints. Paper presented at the ISBA 2019 - 5th IEEE International Conference on Identity, Security and Behavior Analysis, doi:10.1109/ISBA.2019.8778527 |
Abstract: | Automated fingerprint recognition using partial and latent fingerprints employs level 3 features which provide additional information in the absence of sufficient number of level 1 and level 2 features. In this paper, we present a methodology for detecting two level 3 features namely, dots and incipient ridges. Specifically, we have designed a deep convolutional neural network which generates a dot map from the input fingerprint image. Subsequently, post-processing operations are performed on the obtained dot map to identify the coordinates of dots and incipient ridges. The results of our experiments on the publicly available PolyU HRF database demonstrate the effectiveness of the proposed algorithm. © 2019 IEEE. |
URI: | https://doi.org/10.1109/ISBA.2019.8778527 https://dspace.iiti.ac.in/handle/123456789/5188 |
ISBN: | 9781728105321 |
Type of Material: | Conference Paper |
Appears in Collections: | Department of Electrical Engineering |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
Altmetric Badge: