scholarly journals Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

2019 ◽  
Vol 9 (1) ◽  
pp. 3807-3813
Author(s):  
A. Alsubari ◽  
S. A. Hannan ◽  
M. Alzahrani ◽  
R. J. Ramteke
Keyword(s):  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Research Work ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Zernike Moment ◽  
Identification System ◽  
Histogram Of Oriented Gradient ◽  
Composite Feature ◽  
Palm Print

Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

Extraction of Hand Gesture Features for Indian Sign languages using Combined DWT-DCT and Local Binary Pattern

2018 ◽  
Vol 7 (2.24) ◽  
pp. 316
Author(s):  
Muthukumar. K ◽  
Poorani S ◽  
Gobhinath S
Keyword(s):  
Skin Color ◽  
Local Binary Pattern ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Hand Gesture ◽  
Discrete Wavelet ◽  
Sign Languages ◽  
Sobel Operator ◽  
Language Data ◽  
Indian Sign Language

The Hand Gesture system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the Hand features are acquired from the camera and stored in a database along with the Sign languages. In the recognition mode, the hand features are re-acquired from the camera and compared against the stored Indian sign language data to determine the exact signs. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of hand gesture. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the hand gesture is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed.   

scholarly journals CONTENT-BASED IMAGE RETRIEVAL UNTUK PENCARIAN PRODUK PONSEL

2019 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Nickolas Cornelius Siantar ◽  
Jaqnson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Image Retrieval ◽  
Vector Quantization ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Content Based Image Retrieval ◽  
Color Quantization ◽  
Histogram Of Oriented Gradient ◽  
Feature Extractor ◽  
Online Stores

Phone or smartphone and online shop, there is something that cannot be separated with human. There are so many type of smartphones show up in the market that people are confused on which one to get on the online stores. Smartphones recognition is done by using the Histogram of Oriented Gradient to recognize shapes of phones, Color Quantization to recognize the color, and Local Binary Pattern to recognize texture of the phones. The output of the Feature Extractor is a feature vector which is used on the LVQ to process recognize through finding the smallest Euclidean Distance between the trained vectors. The result of this paper is an application that can recognize 16 phone types using the image with the accuracy of 9.6%. Pada saat ini, ponsel dan toko online merupakan sesuatu yang tidak dapat dipisahkan dari manusia. Begitu banyak jenis ponsel bermunculan setiap tahunnya sehingga menyebabkan manusia bingung dalam mengenali ponsel tersebut. Pada program pengenalan ponsel ini digunakan Histogram of Oriented Gradient untuk mengambil fitur berupa bentuk ponsel, Color Quantization untuk mengambil fitur warna, dan Local Binary Pattern untuk mengambil fitur tekstur ponsel. Hasil dari pengambilan fitur berupa fitur vektor yang digunakan pada Learning Vector Quantization untuk proses pengenalan dengan mencari nilai terkecil Euclidean Distance antara vektor fitur dengan vektor bobot terlatih. Hasil dari program pengenalan ini yaitu program dapat melakukan pengenalan terhadap 16 jenis ponsel dengan akurasi sebesar 9.6%.

Extraction of Hand Gesture Features for Indian Sign languages using Combined DWT-DCT and Local Binary Pattern

2018 ◽  
Vol 7 (2.24) ◽  
pp. 316 ◽  
Author(s):  
Muthukumar. K ◽  
Poorani S ◽  
Gobhinath S
Keyword(s):  
Skin Color ◽  
Local Binary Pattern ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Hand Gesture ◽  
Discrete Wavelet ◽  
Sign Languages ◽  
Sobel Operator ◽  
Language Data ◽  
Indian Sign Language

The Hand Gesture system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the Hand features are acquired from the camera and stored in a database along with the Sign languages. In the recognition mode, the hand features are re-acquired from the camera and compared against the stored Indian sign language data to determine the exact signs. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of hand gesture. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the hand gesture is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed. 

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

SELECTION OF CONTROLLABLE PARAMETERS OF THE BINARY DATA ARRAY DETECTION ALGORITHM IN THE INTEGRATED MANAGEMENT SYSTEM

2021 ◽  
Vol 4 (3) ◽  
pp. 11-18
Author(s):  
Khakimjon Zaynidinov ◽  
◽  
Odilbek Askaraliyev
Keyword(s):  
Management System ◽  
Binary Data ◽  
Integrated Management ◽  
Research Work ◽  
Hash Functions ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Identification Algorithm ◽  
Solution Algorithms ◽  
Selection Of

The article discusses the selection of parameters for the algorithm for determining binary data arrays included in the control system, developed by the authors using independent substitution methods. Based on the analysis of the algorithms of non-cryptographic hash functions, the hash function based on the linear matching method was selected as the basis for independent substitution methods. Simplified schemes of algorithms developed for creating and comparing identifiers using a set of basic hash functions are given. An array of binary data was selected and based on the appropriate values for the size of the divisible blocks and the number of basic hashfunctions used for independent substitutions. The selection of binary data arrays in information systems integrated into the management system was done for the purpose of intellectual processing of incoming data. The properties of the array of data entering integrated systems are studied. The authors conducted experimental tests in the selected direction and presented the results of similarity assessment measurements for various parameters of the identification algorithm. In addition, the article conductedexperiments on the object of study using the selected mathematical model, based on the analytical conclusions. Initiator elements are studied and analyzed using a set of hash functions. An algorithm for comparison of selected identifiers has been developed. A generation algorithm has been developed to demonstrate and test the proposed solution. Algorithms based on analysis and experiments, and methods for selecting binary data arrays using the ash function have been experimentally tested. Based on the results, the indicators are shown. Based on the results obtained, the analytical conclusions and problem solutions of the research work were recognized

scholarly journals HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM

2021 ◽  
Vol 25 (01) ◽  
pp. 80-91
Author(s):  
Saba K. Naji ◽  
◽  
Muthana H. Hamd ◽  
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Recognition System ◽  
Manhattan Distance ◽  
Distance Measurements ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Cosine Distance

Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

scholarly journals Sistem Identifikasi Keaslian Uang Kertas Rupiah Menggunakan Metode K-Means Clustering

Techno Com ◽  
2018 ◽  
Vol 17 (2) ◽  
pp. 179-185
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Miladiah Miladiah
Keyword(s):  
Euclidean Distance ◽  
Local Binary Pattern

Keberadaan uang palsu kerap kali beredar di masyarakat. Meskipun peningkatannya tidak secara signifikan namun tetap masyarakat harus berhati – hati dengan oknum yang mampu mengelabui mata awam masyarakat. Solusi yang diberikan oleh pemerintah untuk berhati – hati terhadap uang palsu adalah dengan mengandalkan 3D (dilihat, diraba dan diterawang) namun langkah tersebut belum secara sempurna dapat membedakan uang asli dan palsu. Sehingga perlu adanya sebuah sistem  deteksi olah citra digital. Penelitian ini merancang sebuah sistem pendeteksi keaslian uang kertas rupiah. Metode ektraksi ciri dengan Local Binary Pattern, melalui tahapan pengenalan fitur – fitur pada citra seperti texture permukaan  pada uang kertas dan metode klasifikasi menggunakan K-Means Cluster dengan menghitung centroid dari data yang ada di masing – masing cluster dengan menggunakan rumus persamaan euclidean distance. Pecahan uang yang diteliti adalah pecahan 50.000 dan 100.000 rupiah yang masing berjumlah 30 set data  untuk uang asli dan 20 set data uang palsu yang diperoleh melalui fotocopy warna pecahan uang tadi. Berdasarkan simulasi yang telah dilakukan, untuk deteksi keaslian uang asli dan palsu mampu mencapai akurasi sebesar 96,67% bervariasi tergantung dari jumlah data pelatihan yang dilakukan. Ini menunjukkan bahwa sistem yang dirancang dapat membedakan keaslian uang kertas rupiah.

scholarly journals Speech Recognition of Moroccan Dialect Using Hidden Markov Models

2019 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Mouaz Bezoui
Keyword(s):  
Speech Recognition ◽  
Euclidean Distance ◽  
Speaker Identification ◽  
Arab World ◽  
Training Session ◽  
Arab Countries ◽  
Identification System ◽  
Training Phase ◽  
Standard Arabic ◽  
Testing Phase

<p>This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.</p>

scholarly journals Model of Multilevel Sub-Image to Find the Position of Region of Interest

2016 ◽  
Vol 3 (2) ◽  
pp. 189-196
Author(s):  
Budi Hartono ◽  
Veronica Lusiana
Keyword(s):  
Euclidean Distance ◽  
Region Of Interest ◽  
Image Data ◽  
Image Feature ◽  
Experimental Result ◽  
Main Attention ◽  
Image Content ◽  
Query Image ◽  
Image Object

Searching image is based on the image content, which is often called with searching of image object. If the image data has similarity object with query image then it is expected the searching process can recognize it. The position of the image object that contains an object, which is similar to the query image, is possible can be found at any positionon image data so that will become main attention or the region of interest (ROI). This image object can has different wide image, which is wider or smaller than the object on the query image. This research uses two kinds of image data sizes that are in size of 512X512 and in size of 256X256 pixels.Through experimental result is obtained that preparing model of multilevel sub-image and resize that has same size with query image that is in size of 128X128 pixels can help to find ROI position on image data. In order to find the image data that is similar to the query image then it is done by calculating Euclidean distance between query image feature and image data feature.

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