scholarly journals Research and implementation of integrated fingerprint classification system based on backtracking feedback

2018 ◽  
Vol 246 ◽  
pp. 03030
Author(s):  
Han Jian Ning
Keyword(s):  
Classification System ◽  
Recognition Rate ◽  
Research Direction ◽  
Recognition System ◽  
Feedback Mechanism ◽  
Fingerprint Recognition ◽  
Fingerprint Classification ◽  
Intelligent Recognition ◽  
Important Research Direction ◽  
Experimental Comparisons

Fingerprint classification has always been an important research direction in the field of intelligent recognition. Based on the method of fingerprint classifier integration, the backtracking feedback mechanism is introduced, and a fingerprint classification system with high recognition rate is designed. Through the use of 1000 fingerprint images in the fingerprint library to test, The system show the recognition results due to the current Kalle Karu, anli K.jain design of a variety of fingerprint recognition system. Through a series of experimental comparisons, it is proved that the fingerprint classification recognition system with the feedback mechanism has better ability of fingerprint recognition, and greatly reduces the error rate of system recognition.

scholarly journals Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 185-192
Author(s):  
Feng Shan ◽  
◽  
Hui Sun ◽  
Xiaoyun Tang ◽  
Weiwei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Rate ◽  
Test Site ◽  
Recognition System ◽  
Image Features ◽  
Convolution Neural Network ◽  
Digital Display ◽  
Industrial Control ◽  
Intelligent Recognition

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

scholarly journals A Study on Machine Learning Approach for Fingerprint Recognition System

2019 ◽  
Vol 5 (11) ◽  
Author(s):  
Aayushi Tamrakar ◽  
Neetesh Gupta
Keyword(s):  
Rejection Rate ◽  
Recognition System ◽  
Extraction Methods ◽  
Acceptance Rate ◽  
Fingerprint Recognition ◽  
Fingerprint Classification ◽  
Machine Learning Approach ◽  
Optimum Approach ◽  
Authentication System ◽  
Recognition Technique

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. Authentication system like fingerprint recognition is most commonly used biometric authentication system. Fingerprint method of identification is the oldest and widely used method of authentication used in biometrics. There are several reasons like displacement of finger during scanning, environmental conditions, behavior of user, etc., which causes the reduction in acceptance rate during fingerprint recognition. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. Fingerprint Recognition system designed uses various techniques in order to reduce the False Acceptance Rate (FAR) and False Rejection Rate (FRR) and to enhance the performance of the system. This paper reviews the fingerprint classification including feature extraction methods and learning models for proper classification to label different fingerprints. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system.

Face Recognition System Based on HWME

2013 ◽  
Vol 278-280 ◽  
pp. 2006-2011
Author(s):  
Wei Ping Hu ◽  
Hui Wen Deng
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Research Direction ◽  
Recognition System ◽  
Face Recognition System ◽  
Key Points ◽  
The Face ◽  
New Research

The limitations of current researches on face recognition is analyzed firstly that the researchers failed to find a compromise between recognition rate and speed. Then open complex giant system and the Hall for Workshop of Meta-synthetic Engineering are introduced. After analyzing features of the face recognition, a new research direction is put forward that face recognition can be treated as an open complex giant problem and can be solved through the Hall for Workshop of Meta-synthetic Engineering (HWME) first designed by Qian Xuesen(Hsueshen Tsien). Finally, a framework of face recognition system based on HWME is proposed and four key points of the system are discussed.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Analysis of Fingerprint Recognition System Using Neural Network

2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Fingerprint Recognition

scholarly journals 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2021 ◽  
Author(s):  
Wei Jia ◽  
Wei Xia ◽  
Yang Zhao ◽  
Hai Min ◽  
Yan-Xiang Chen
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Research Direction ◽  
Palmprint Recognition ◽  
Neural Architecture ◽  
Development Direction ◽  
Vein Recognition ◽  
Palm Vein ◽  
2D And 3D ◽  
Important Research Direction

AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

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