DFRS: A Large-Scale Distributed Fingerprint Recognition System Based on Redis

2016 ◽  
pp. 138-149 ◽  
Author(s):  
Bing Li ◽  
Zhen Huang ◽  
Jinbang Chen ◽  
Yifan Yuan ◽  
Yuxing Peng
Keyword(s):  
Large Scale ◽  
Recognition System ◽  
Fingerprint Recognition

scholarly journals Spectral Minutiae Fingerprint Recognition System

2014 ◽  
pp. 103-108
Author(s):  
S. Shanawaz Basha ◽  
N. Musrat Sultana
Keyword(s):  
Large Scale ◽  
Reduction Rate ◽  
Principal Component ◽  
Rejection Rate ◽  
Recognition System ◽  
Feature Reduction ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Behavioral Characteristics ◽  
Template Size

Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics, such as faces, finger prints, iris, and gait. In this paper, we focus on the application of finger print recognition system. The spectral minutiae fingerprint recognition is a method to represent a minutiae set as a fixedlength feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%.With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system, this fast operation renders our system suitable for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems like, police patrolling, airports etc,. The spectral minutiae representation system tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

Fingerprint Scaling for Sensor Interoperability

2013 ◽  
Vol 303-306 ◽  
pp. 908-911 ◽  
Author(s):  
Jian Min Guo ◽  
Chun Xiao Ren ◽  
Yu Xiao Wu
Keyword(s):  
Delaunay Triangulation ◽  
Large Scale ◽  
Estimation Method ◽  
Recognition System ◽  
Experimental Results ◽  
Fingerprint Recognition ◽  
Scaling Technique ◽  
Scaling Methods ◽  
System Module ◽  
Positive Results

The need for sensor interoperability has increased tremendously in many large-scale fingerprint applications such as e-commerce, welfare-disbursement and e-education. In this paper, we present fingerprint scaling, an innovative fingerprint system module, which resizes the fingerprint images to solve the problem of sensor interoperability with minimum modification to existing system. In template-based scaling methods, we have developed an estimation method using Delaunay triangulation algorithm. The efficacy of the scaling technique has been assessed by embedding a scaling module into a traditional fingerprint recognition system. Our experiments have shown very positive results. Experimental results indicated that the accuracy and robustness of fingerprint system can be improved effectively by embedding such a scaling module into the traditional framework of fingerprint systems under multi-sensor situation.

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 Reliability of a Visual Recognition System for Detection of Johnsongrass(Sorghum halepense) in Corn

2011 ◽  
Vol 25 (4) ◽  
pp. 645-651 ◽  
Author(s):  
Dionisio Andújar ◽  
Ángela Ribeiro ◽  
Cesar Fernández-Quintanilla ◽  
José Dorado
Keyword(s):  
Large Scale ◽  
Visual Recognition ◽  
Visual Detection ◽  
Cropping Systems ◽  
Experimental Period ◽  
Recognition System ◽  
Infested Area ◽  
Net Returns ◽  
Cropping Season ◽  
Combine Harvester

The feasibility of visual detection of weeds for map-based patch spraying systems needs to be assessed for use in large-scale cropping systems. The main objective of this research was to evaluate the reliability and profitability of using maps of Johnsongrass patches constructed at harvest to predict spatial distribution of weeds during the next cropping season. Johnsongrass patches visually were assessed from the cabin of a combine harvester in three corn fields and were compared with maps obtained in the subsequent year prior to postemergence herbicide application. There was a good correlation (71% on average) between the position of Johnsongrass patches on the two maps (fall vs. spring). The highest correlation (82%) was obtained with relatively large infestations, whereas the lowest (58%) was obtained when the infested area was smaller. Although the relative positions of the patches remained almost unchanged from 1 yr to the next, the infested area increased in all fields during the 4-yr experimental period. According to our estimates, using a strategy based on spraying full rates of herbicides to patches recorded in the map generated in the previous fall resulted in higher net returns than spraying the whole field, either at full or half rate. This site-specific strategy resulted in an average 65% reduction in the volume of herbicide applied to control this weed.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

scholarly journals School Debit Transaction Using Fingerprint Recognition System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Wai Kit Wong ◽  
Thu Soe Min ◽  
Shi Enn Chong
Keyword(s):  
Labour Force ◽  
Recognition System ◽  
Payment System ◽  
Fingerprint Recognition ◽  
Biometric Technology ◽  
Cash Payment ◽  
User Need ◽  
Minutiae Matching

This paper proposed a fingerprint based school debit transaction system using minutiae matching biometric technology. This biometric cashless transaction system intensely shortens the luncheon line traffic and labour force compared to conventional cash payment system. Furthermore, contrast with card cashless transaction system, fingerprint cashless transaction system with benefit that user need not carry additional identification object and remember lengthy password. The implementation of this cashless transaction system provides a more organize, reliable and efficient way to operate the school debit transaction system. 

scholarly journals U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data

2021 ◽  
Vol 11 (21) ◽  
pp. 10079
Author(s):  
Muhammad Firoz Mridha ◽  
Abu Quwsar Ohi ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid ◽  
Md. Rashedul Islam ◽  
...  
Keyword(s):  
Speaker Recognition ◽  
Large Scale ◽  
English Language ◽  
Domain Adaptation ◽  
Recognition System ◽  
Extraction Process ◽  
Unlabeled Data ◽  
Training Strategy ◽  
Speech Segment ◽  
Recognition Systems

Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task. The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset. As the embedding systems are pre-trained, the performance of speaker recognition models greatly depends on domain adaptation policy, which may reduce if trained using inadequate data. This paper introduces a speaker recognition strategy dealing with unlabeled data, which generates clusterable embedding vectors from small fixed-size speech frames. The unsupervised training strategy involves an assumption that a small speech segment should include a single speaker. Depending on such a belief, a pairwise constraint is constructed with noise augmentation policies, used to train AutoEmbedder architecture that generates speaker embeddings. Without relying on domain adaption policy, the process unsupervisely produces clusterable speaker embeddings, termed unsupervised vectors (u-vectors). The evaluation is concluded in two popular speaker recognition datasets for English language, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to illustrate the diversity of the domain shifts for speaker recognition systems. Finally, we conclude that the proposed approach achieves satisfactory performance using pairwise architectures.

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