scholarly journals A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Haryati Jaafar ◽  
Salwani Ibrahim ◽  
Dzati Athiar Ramli
Keyword(s):  
Image Enhancement ◽  
Current Trend ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Recognition System ◽  
Training Data ◽  
Hand Tracking ◽  
Fast Computation ◽  
Palm Print

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-basedknearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

2017 ◽  
Vol 8 (2) ◽  
pp. 56-72 ◽  
Author(s):  
Aderonke Lawal ◽  
Segun Aina ◽  
Samuel Okegbile ◽  
Seun Ayeni ◽  
Dare Omole ◽  
...  
Keyword(s):  
Region Of Interest ◽  
High Sensitivity ◽  
Histogram Equalization ◽  
Recognition System ◽  
Thinning Algorithm ◽  
Feature Vectors ◽  
Palm Print ◽  
Vein Recognition ◽  
High Recognition Accuracy ◽  
Palm Vein

Biometrics is a technology for recognition under which Palm vein recognition stems. They are of crucial importance in various applications of high sensitivity. This article develops a palm vein recognition model, based on derived pattern and feature vectors. All the palm print images used in this work were obtained from CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database). First, a Region of Interest (ROI) was identified and extracted from the palm print images. Next, Histogram Equalization was used to enhance the area of the palm print image in the Region of Interest. The enhanced image obtained was subjected to the Zhang Suen's Thinning Algorithm to extract appropriate features in the palm print images needed for authentication. The features derived based on this vascular pattern thinning algorithm which are then compared and evaluated to carry out ‘matching'. The Pattern Matching itself was done using the Euclidean Distance for subsequent matching. The model was designed using UML, and implemented with C# and MS SQL on Microsoft Visual Studio platform. The developed system was evaluated based on False Acceptance, False Rejection and Equal Error Rate (EER) values obtained from the system. The results of testing and evaluation show that the developed system has achieved high recognition accuracy.

scholarly journals Continuous kannada speech segmentation and speech recognition based on threshold using MFCC And VQ

2019 ◽  
Vol 9 (6) ◽  
pp. 4684
Author(s):  
Vanajakshi Puttaswamy Gowda ◽  
Mathivanan Murugavelu ◽  
Senthil Kumaran Thangamuthu
Keyword(s):  
Speech Recognition ◽  
Language Processing ◽  
Speech Signal ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Speech Segmentation ◽  
Significant Feature ◽  
Mel Frequency Cepstral Coefficients ◽  
Simple Method

<p><span>Continuous speech segmentation and its  recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends  on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal  for recognition system is quite exciting for researchers. In this paper proposed method  is  divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out  by computing  average short term energy and its spectral centroid coefficients of  the speech signal present in the specified window. The segmented outputs are completely  meaningful  segmentation  for different scenarios with less segmentation error. The second part of the method is speech recognition by extracting less number Mel frequency cepstral coefficients with less  number of codebooks  using vector quantization .In this recognition is completely based on threshold value.This threshold setting is a challenging task however the simple method is used to achieve better recognition rate.The experimental results shows more efficient  and effective segmentation    with high recognition rate for any continuous context based kannada speech signal with different accents for male and female than the existing methods and also used minimal feature dimensions for training data.</span></p>

Hand gesture recognition via image processing techniques and deep CNN

2020 ◽  
Vol 39 (3) ◽  
pp. 4405-4418
Author(s):  
Yao-Liang Chung ◽  
Hung-Yuan Chung ◽  
Wei-Feng Tsai
Keyword(s):  
Skin Color ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Training Data ◽  
Background Information ◽  
Hand Gestures ◽  
Data Set ◽  
Practical Applications ◽  
Deep Cnn ◽  
Hand Region

In the present study, we sought to enable instant tracking of the hand region as a region of interest (ROI) within the image range of a webcam, while also identifying specific hand gestures to facilitate the control of home appliances in smart homes or issuing of commands to human-computer interaction fields. To accomplish this objective, we first applied skin color detection and noise processing to remove unnecessary background information from the captured image, before applying background subtraction for detection of the ROI. Then, to prevent background objects or noise from influencing the ROI, we utilized the kernelized correlation filters (KCF) algorithm to implement tracking of the detected ROI. Next, the size of the ROI image was resized to 100×120 and input into a deep convolutional neural network (CNN) to enable the identification of various hand gestures. In the present study, two deep CNN architectures modified from the AlexNet CNN and VGGNet CNN, respectively, were developed by substantially reducing the number of network parameters used and appropriately adjusting internal network configuration settings. Then, the tracking and recognition process described above was continuously repeated to achieve immediate effect, with the execution of the system continuing until the hand is removed from the camera range. The results indicated excellent performance by both of the proposed deep CNN architectures. In particular, the modified version of the VGGNet CNN achieved better performance with a recognition rate of 99.90% for the utilized training data set and a recognition rate of 95.61% for the utilized test data set, which indicate the good feasibility of the system for practical applications.

scholarly journals The investigation on defect recognition system using gaussian smoothing and template matching approach

2020 ◽  
Vol 18 (2) ◽  
pp. 812
Author(s):  
M. H. Harun ◽  
M. F. Yaakub ◽  
A. F. Z. Abidin ◽  
A. H. Azahar ◽  
M. S. M. Aras ◽  
...  
Keyword(s):  
Defect Detection ◽  
Template Matching ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Recognition System ◽  
Automated Inspection ◽  
Gaussian Smoothing ◽  
Important Region ◽  
Detection Approach ◽  
Defect Recognition

<p>This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features is proposed in determining better image processing. Template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. This system does lessen execution time, yet additionally produce high precision in deformity location rate. It is discovered that the proposed framework can give precision at 95.77% recognition rate in recognizing imperfection for gluing application.</p>

scholarly journals Smart image recognition system based on machine vision

2021 ◽  
Vol 2083 (4) ◽  
pp. 042049
Author(s):  
Lei Li ◽  
Fenggang Liu
Keyword(s):  
Machine Vision ◽  
High Speed ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Accurate Method ◽  
Recognition System ◽  
Experimental Results ◽  
Movement Speed ◽  
Qr Code ◽  
Two Dimensional

Abstract This paper proposes an efficient and accurate method of two-dimensional code recognition for industrial actual projects, and develops a high-speed and batch two-dimensional code recognition system based on machine vision. Firstly, according to the position of the QR code in the target subspace, a method to locate the region of interest of each QR code by using geometric relationship and batch processing QR code is proposed. On this basis, Gaussian noise is added to simulate the possible noise in production practice, and the anti-noise ability of the system is evaluated. Finally, the relationship between system recognition rate and QR code movement speed is analyzed and the experimental results are compared. The experimental results show that the system can meet the requirement of real-time online detection.

scholarly journals Real-Time Indonesian Language Speech Recognition with MFCC Algorithms and Python-Based SVM

2019 ◽  
Vol 3 (2) ◽  
pp. 55
Author(s):  
Wening Mustikarini ◽  
Risanuri Hidayat ◽  
Agus Bejo
Keyword(s):  
Speech Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
Human Voice ◽  
Average Accuracy ◽  
Speech Data ◽  
Mel Frequency Cepstral Coefficient

Abstract — Automatic Speech Recognition (ASR) is a technology that uses machines to process and recognize human voice. One way to increase recognition rate is to use a model of language you want to recognize. In this paper, a speech recognition application is introduced to recognize words "atas" (up), "bawah" (down), "kanan" (right), and "kiri" (left). This research used 400 samples of speech data, 75 samples from each word for training data and 25 samples for each word for test data. This speech recognition system was designed using Mel Frequency Cepstral Coefficient (MFCC) as many as 13 coefficients as features and Support Vector Machine (SVM) as identifiers. The system was tested with linear kernels and RBF, various cost values, and three sample sizes (n = 25, 75, 50). The best average accuracy value was obtained from SVM using linear kernels, a cost value of 100 and a data set consisted of 75 samples from each class. During the training phase, the system showed a f1-score (trade-off value between precision and recall) of 80% for the word "atas", 86% for the word "bawah", 81% for the word "kanan", and 100% for the word "kiri". Whereas by using 25 new samples per class for system testing phase, the f1-score was 76% for the "atas" class, 54% for the "bawah" class, 44% for the "kanan" class, and 100% for the "kiri" class.

Preprocessing for Local Bianry Pattern Based Face Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1434-1437
Author(s):  
Ning Xing ◽  
Lan Xian Gui
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Rate Increase ◽  
Recognition Rate ◽  
Histogram Equalization ◽  
Recognition System ◽  
Processing Methods ◽  
Gaussian Smoothing ◽  
Sobel Operator ◽  
Face Recognition System

This paper studies the pre-processing methods on Local Binary Pattern (LBP) for face recognition. Three methods are well investigated, such as Gaussian smoothing, histogram equalization and Sobel operator. The extensive experiments on FERET database show that the best recognition rate increase is achieved by combination of histogram equalization, Gaussian smoothing, and Sobel with LBP. The conclusion achieved in the paper is useful for real face recognition system.

Study of Face Orientation Recognition Based on Neural Network

2018 ◽  
Vol 32 (11) ◽  
pp. 1856015 ◽  
Author(s):  
Suping Li ◽  
Zhanfeng Wang ◽  
Jing Wang
Keyword(s):  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Training Data ◽  
Future Research ◽  
Identification System ◽  
Bp Network ◽  
Feature Vectors ◽  
And Performance ◽  
The Stability

Learning vector quantization (LVQ) network and back-propagation (BP) network are constructed easily making use of MATLAB toolbox on the basis of maintaining the recognition rate. Face images are randomly selected from images set as training data of LVQ network and BP network. LVQ algorithm and BP algorithm are used to train network. The automatic recognition of face orientation is realized when the system obtains convergence network. First, all images are processed by edge detection. Then feature vectors representing position of the eye were extracted from edge detected images. Feature vectors of training set are sent to network to adjust the parameters which ensures the convergence speed and performance of the network. Experimental results show that the constructed LVQ network and BP network can judge face orientation according to feature vectors of input images. Generally, the recognition rate of LVQ network is higher than that of BP network. The LVQ network and BP network are both feasible and effective for face orientation recognition to some extent. The advantage of this work is that the recognition system is efficient and easy to promote. This paper focuses on how to use MATLAB easily to design identification network rather than the complexity of identification system. The future research will focus on the stability and robustness of recognition network.

scholarly journals Palm print recognition based on harmony search algorithm

2021 ◽  
Vol 11 (5) ◽  
pp. 4113
Author(s):  
Raniah Ali Mustafa ◽  
Haitham Salman Chyad ◽  
Rafid Aedan Haleot
Keyword(s):  
Hong Kong ◽  
Gaussian Distribution ◽  
Search Algorithm ◽  
Harmony Search ◽  
Region Of Interest ◽  
Recognition System ◽  
Harmony Search Algorithm ◽  
Identification Methods ◽  
Palm Print ◽  
College Of Engineering

Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.

Intelligent Technique for Human Authentication using Fusion of Finger and Dorsal Hand Veins

2021 ◽  
Vol 18 ◽  
pp. 91-101
Author(s):  
Mona A. Ahmed ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Median Filter ◽  
Recognition Rate ◽  
Wiener Filter ◽  
Region Of Interest ◽  
Principal Component ◽  
Histogram Equalization ◽  
K Nearest Neighbors ◽  
Intelligent Technique ◽  
Dorsal Hand ◽  
High Recognition Accuracy

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using intelligent technique to authenticate human by fusion of finger and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from finger and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve finger and dorsal hand veins images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 96.8%.

Sign in / Sign up

Export Citation Format

Share Document