scholarly journals A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 730
Author(s):  
Srđan Barzut ◽  
Milan Milosavljević ◽  
Saša Adamović ◽  
Muzafer Saračević ◽  
Nemanja Maček ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Error Correction Codes ◽  
Fingerprint Matching ◽  
Biometric Data ◽  
Protection Mechanism ◽  
Authentication Mechanism ◽  
Novel Approach ◽  
Access Controls ◽  
Biometric Cryptosystem

Modern access controls employ biometrics as a means of authentication to a great extent. For example, biometrics is used as an authentication mechanism implemented on commercial devices such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on the fuzzy commitment scheme and convolutional neural networks. One of its main contributions is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on a convolutional neural network, and designed to generate fixed-length templates. By converting templates into the binary domain, we developed the biometric cryptosystem that can be used in key-release systems or as a template protection mechanism in fingerprint matching biometric systems. The problem of biometric data variability is marginalized by applying the secure block-level Bose–Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation shows significant performance gains when compared to other texture-based fingerprint matching and biometric cryptosystems.

scholarly journals CROSS-LANGUAGE TEXT CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS FROM SCRATCH

2017 ◽  
Vol 2 ◽  
pp. 24-33 ◽  
Author(s):  
Musbah Zaid Enweiji ◽  
Taras Lehinevych ◽  
Аndrey Glybovets
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Current Method ◽  
Classification Model ◽  
The Novel ◽  
Novel Approach ◽  
Multilingual Learning ◽  
Cross Language ◽  
Language Text ◽  
Language Classification

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

scholarly journals 3D Face Synthesis Driven by Personality Impression

2019 ◽  
Vol 33 ◽  
pp. 1707-1714
Author(s):  
Yining Lang ◽  
Wei Liang ◽  
Yujia Wang ◽  
Lap-Fai Yu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Computer Games ◽  
Virtual World ◽  
Second Step ◽  
Deep Convolutional Neural Networks ◽  
Virtual Characters ◽  
3D Face ◽  
Novel Approach ◽  
Face Synthesis

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces.

Novel approach to estimate vertical scale of fluctuation based on CPT data using convolutional neural networks

2021 ◽  
pp. 106342
Author(s):  
Jin-Zhang Zhang ◽  
Kok Kwang Phoon ◽  
Dong-Ming Zhang ◽  
Hong-Wei Huang ◽  
Chong Tang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Novel Approach ◽  
Vertical Scale ◽  
Scale Of Fluctuation
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