FINGERPRINT MATCHING USING ARTMAP

2004 ◽  
Vol 12 (supp02) ◽  
pp. 15-30
Author(s):  
C. P. SUMATHI ◽  
T. SANTHANAM ◽  
K. S. EASWARAKUMAR ◽  
BHANU PRASAD
Keyword(s):  
Neural Network ◽  
Memory Capacity ◽  
Performance Degradation ◽  
Fingerprint Matching ◽  
Gray Level ◽  
Large Database ◽  
Minutiae Extraction ◽  
Concurrent Processing ◽  
Image Pixels

This paper deals with the possibility of using ARTMAP neural network for searching fingerprint patterns from a large database. ARTMAP has the ability to perform concurrent processing, to learn fast, and to make decisions. Since ARTMAP learning is self-stabilizing, it can continue to learn from one or more databases, without performance degradation, until its full memory capacity is utilized. Generally, fingerprint matching is based on local ridge characteristics, and its efficiency depends on minutiae extraction. The proposed method uses only gray level values of the image pixels along with its neighboring ones, instead of ridge features.

scholarly journals End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

2020 ◽  
Vol 7 ◽  
Author(s):  
Uttam U. Deshpande ◽  
V. S. Malemath ◽  
Shivanand M. Patil ◽  
Sushma V. Chaugule
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fingerprint Identification ◽  
Fingerprint Matching ◽  
Latent Fingerprint ◽  
Minutiae Extraction ◽  
End To End ◽  
Enhancement Algorithm

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

FINGERPRINT MATCHING WITH A NEURAL NETWORK

2016 ◽  
Author(s):  
Bernardo Sotto-Maior Peralva ◽  
Fernando Miranda Vieira Xavier ◽  
Augusto Santiago Cerqueira ◽  
David Sérgio Adães Gouvea ◽  
Marcos Fidelis Costa Campos
Keyword(s):  
Neural Network ◽  
Fingerprint Matching

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

ICT Express ◽  
2021 ◽  
Author(s):  
Fitri Utaminingrum ◽  
Syam Julio A. Sarosa ◽  
Corina Karim ◽  
Femiana Gapsari ◽  
Randy Cahya Wihandika
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gray Level ◽  
Occurrence Matrix

AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL

2021 ◽  
Vol 21 (01) ◽  
pp. 2150005
Author(s):  
ARUN T NAIR ◽  
K. MUTHUVEL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Vessel Segmentation ◽  
Deep Convolutional Neural Network ◽  
Gray Level ◽  
Pass Filter ◽  
Blood Vessel Segmentation

Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

2021 ◽  
pp. 1-7
Author(s):  
Lazar M. Davidovic ◽  
Jelena Cumic ◽  
Stefan Dugalic ◽  
Sreten Vicentic ◽  
Zoran Sevarac ◽  
...  
Keyword(s):  
Neural Network ◽  
Saccharomyces Cerevisiae ◽  
Random Trees ◽  
Computational Method ◽  
Yeast Cells ◽  
Matrix Analysis ◽  
Gray Level ◽  
Cell Nuclei ◽  
Intact Cells ◽  
Occurrence Matrix

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

2018 ◽  
Vol 233 (10) ◽  
pp. 2467-2478 ◽  
Author(s):  
Radhika Raveendran ◽  
Apoorva Suresh ◽  
Vignesh Rajaram ◽  
Shankar C Subramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confidence Interval ◽  
Prediction Error ◽  
Performance Degradation ◽  
Dynamics Simulation ◽  
Brake System ◽  
Road Vehicles ◽  
Prediction Scheme ◽  
Artificial Neural

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

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