Facial Identification Based on Transform Domains for Images and Videos

The current development of microcontroller technology can be used to build a presence system for employees. The employee attendance system uses radio frequency identification and facial identification which is designed and built to make it easier to do attendance data recording, so that the data obtained can be precise and accurate. Data collection techniques, namely by interview and observation. The application development process uses the PHP and Python programming languages with Visual Studio Code software applications, Arduino Uno, MySQL software as a database server, and XAMPP as a support. The input used in this system is the employee's personal data and the results of employee face data retrieval which are stored in the .jpg format. The faces taken were taken from 4 people where each face was taken 20 face samples. The results are in the form of web and applications that will provide solutions to existing problems. The conclusion of this application makes it easy to do the recording and attendance, and minimize the fraud committed by employees. Retrieval of face data was taken as much as 20 data with the highest level of accuracy was 87% when the presence test was carried out.

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Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c1066.1183s319 ◽

2019 ◽

Vol 8 (3S3) ◽

pp. 280-290

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Histogram Equalization ◽

Extraction Process ◽

Input Image ◽

Suggested Approach ◽

Biometric Systems ◽

Recognition Systems ◽

The Right ◽

Facial Identification

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

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New Orthogonal Transforms for Signal and Image Processing

Applied Sciences ◽

10.3390/app11167433 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7433

Author(s):

Andrzej Dziech

Keyword(s):

Symmetric Matrix ◽

Sparse Matrices ◽

Orthogonal Expansion ◽

Orthogonal Matrices ◽

Exponential Form ◽

Orthogonal Transforms ◽

Hadamard Transforms ◽

The Given ◽

Transform Domains ◽

Signal Approximation

In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms. The simplicity of calculating the forward and inverse transforms is one of the important features of the presented approach. The conditions for creating symmetric, orthogonal matrices are defined. It is shown that for the selection of the elements of an orthogonal matrix that meets the given conditions, it is necessary to select only a limited number of elements. The general form of the orthogonal, symmetric matrix having an exponential form is also presented. Orthogonal basis functions based on the created matrices can be used for orthogonal expansion leading to signal approximation. An exponential form of orthogonal, sparse matrices with variable parameters is also created. Various versions of orthogonal transforms related to the created full and sparse matrices are proposed. Fast computation of the presented transforms in comparison to fast algorithms of selected orthogonal transforms is discussed. Possible applications for signal approximation and examples of image spectrum in the considered transform domains are presented.

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Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

Information Technology And Control ◽

10.5755/j01.itc.49.3.23998 ◽

2020 ◽

Vol 49 (3) ◽

pp. 299-307

Author(s):

Zengguo Sun ◽

Rui Shi ◽

Wei Wei

Keyword(s):

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Shearlet Transform ◽

Local Means ◽

New Type ◽

Non Local ◽

Homogeneous Regions ◽

Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

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