Personal Identity Identification and Authentication Statistics Based on Countermeasures against Spoofing

To make the online banking system secure, a three-step authentication method using the face, iris, and fingerprint is used. To improve security, spoofing detection has also been included. A spoofing attack happens when someone attempts to impersonate someone else and gets unauthorized access. The face and iris are captured by the camera, while the fingerprint is captured by the fingerprint module. Gray Level Co-occurrence Matrix is used to extract a picture of a person's face, iris, and fingerprint (GLCM). A reference module is constructed using the extracted picture so that the input may be compared to it, and if the input matches the module, a one-time password (OTP) is delivered to the user's mobile phone. Only the online transaction may take place if the OTP is entered correctly. Otherwise, the account holder will get an alert message. This method allows for a very secure online transaction.

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Pengenalan Ras Berdasarkan Hidung Dan Mulut Menggunakan Gray Level Co-Occurrence Matrix

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021844366 ◽

2021 ◽

Vol 8 (4) ◽

pp. 729

Author(s):

Ema Rachmawati ◽

Nur Azizah Agustina ◽

Febryanti Sthevanie

Keyword(s):

Random Forest ◽

System Performance ◽

Experimental Results ◽

Gray Level ◽

The Face ◽

Combined Features ◽

Occurrence Matrix ◽

Large Groups ◽

Performance Of Race

Ras dapat digunakan untuk mengkategorikan manusia dalam populasi atau kelompok besar. Oleh karena itu, pengenalan ras dapat berguna untuk mempermudah dalam mengidentifikasi seseorang dan membantu dalam mempersempit lingkup pencarian. Penggunaan wajah sebagai dasar pengenalan ras mengarahkan penelitian pada identifikasi penggunaan bagian wajah yang berpengaruh signifikan terhadap kinerja pengenalan ras. Pada penelitian ini bagian wajah berupa hidung dan mulut diidentifikasi untuk digunakan sebagai dasar pengenalan ras Mongoloid, Kaukasoid, dan Negroid. Ciri Gray Level Co-occurrence Matrix (GLCM) diekstrak dari bagian hidung dan mulut untuk selanjutnya diklasifikasi menggunakan Random Forest. Hasil eksperimen menunjukkan bahwa penggunaan ciri gabungan dari hidung dan mulut mampu menghasilkan kinerja sistem yang paling baik jika dibandingkan penggunaan hidung atau mulut saja. AbstractRace can be used to categorize humans in populations or large groups. Therefore, racial recognition can be useful to make it easier to identify a person and help narrow the scope of the search. The use of faces as a basis for race recognition directs research on identifying the use of facial parts that significantly influence the performance of race recognition. In this study, the face parts of the nose and mouth were identified to be used as a basis for the recognition of the Mongoloid, Caucasoid, and Negroid races. The Gray Level Co-occurrence Matrix (GLCM) feature is extracted from the nose and mouth to be classified using Random Forest. The experimental results show that the use of combined features of the nose and mouth is able to produce the best system performance compared to the use of the nose or mouth only.

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Russian Banking System under Conditions of Global Crisis

Voprosy Ekonomiki ◽

10.32609/0042-8736-2009-7-4-14 ◽

2009 ◽

pp. 4-14 ◽

Cited By ~ 1

Author(s):

G. Gref ◽

K. Yudaeva

Keyword(s):

Banking System ◽

Commodity Prices ◽

Financial Sector ◽

Banking Crises ◽

Economic Recovery ◽

Balance Sheets ◽

Current Economic Crisis ◽

Risk Appetite ◽

The Us ◽

The Face

Problems in the financial sector were at the core of the current economic crisis. Therefore, economic recovery will only become sustainable after taking care of the major weaknesses in the financial sector. This conclusion is relevant both for the US and UK - the two countries where crisis has started, and for other economies which financial institutions turned out to be fragile in the face of the swings in the risk appetite. Russia is one of the countries where the crisis has revealed serious deficiency in the financial sector. Our study of 11 banking crises during the last 25-30 years shows that sustainable economic recovery and decrease in the dependence on commodity prices will be virtually impossible without cleaning of balance sheets and capitalization of the financial sector.

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The strength of the Lebanese banking system in the face of political instability. (c2008)

10.26756/th.2008.1 ◽

2008 ◽

Author(s):

Maya T. Hobeika

Keyword(s):

Political Instability ◽

Banking System ◽

The Face

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Detection of image region-duplication forgery based on gray level co-occurrence matrix

Journal of Computer Applications ◽

10.3724/sp.j.1087.2011.01628 ◽

2012 ◽

Vol 31 (6) ◽

pp. 1628-1630

Author(s):

Jia-jia OU ◽

Bi-ye CAI ◽

Bing XIONG ◽

Feng LI

Keyword(s):

Gray Level ◽

Image Region ◽

Region Duplication ◽

Occurrence Matrix

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Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

Recent Patents on Engineering ◽

10.2174/1872212112666180601085544 ◽

2019 ◽

Vol 13 (2) ◽

pp. 136-141 ◽

Cited By ~ 2

Author(s):

Abhisek Sethy ◽

Prashanta Kumar Patra ◽

Deepak Ranjan Nayak

Keyword(s):

Feature Extraction ◽

Random Forest ◽

Character Recognition ◽

Recognition Rate ◽

Discrete Wavelet ◽

Gray Level ◽

Handwritten Character Recognition ◽

Handwritten Character ◽

Wide Range ◽

Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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