scholarly journals Signature Recognition Based on Discrete Wavelet Transform

2019 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sivana Salahadin Muhamad ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Personal Identification ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Human Identity ◽  
Processing Stage ◽  
Signature Recognition ◽  
Developing Area

Personal identification is an actively developing area of research. Human signature is a vital biometric attribute which can be used to authenticate human identity. There are many approaches to recognize signature with a lot of researches. The aim of this research is to introduce an efficient approach for signature recognition. This approach starts with the process the acquired signatures and stores these signatures in the database to be ready for verification. The collection of signature data based on collecting samples of 10 people and 10 signatures for each person through traditional ink stamp method. These signatures are digitized to be ready for processing. Many steps are applied to the acquired images to perform the pre-processing stage. The proposed approach based on discrete wavelet transforms to extract significant features from each signature image. Pre-processing is applied at the beginning of this approach to avoid any unwanted noise. This approach consists of many steps: Data acquisition, pre-processing, signature registration, and feature extraction. High recognition rate results (100%) are obtained through applying this approach.

scholarly journals Signature Recognition using 2D Discrete Wavelet Transforms

2020 ◽  
Vol 9 (7) ◽  
pp. 528-532
Keyword(s):  
Wavelet Transforms ◽  
Fourier Transforms ◽  
Absolute Difference ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Statistical Parameters ◽  
Person Identification ◽  
Localization Property ◽  
Signature Recognition ◽  
The One

Signature recognition is one of the most secure techniques used for person identification. Wavelet transforms possess an extra time localization property over Fourier transforms, despite both are frequency localized. That is why; it is more useful to analyze the one dimensional and two dimensional signals both. First of all the features of a signature are extracted and then matching is performed. We have proposed feature extraction technique using discrete wavelet transforms level-1 and matching of signatures are performed using some statistical parameters of discrete wavelet coefficients using matching percentage, sum of absolute difference, mean square error and city block distance.

scholarly journals Face Recognition Use Local Image Dataset and Correlation Technique

2021 ◽  
Vol 5 (2) ◽  
pp. 26-31
Author(s):  
Dana Faiq Abd
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Correlation Technique ◽  
Final Decision ◽  
Accuracy Rate ◽  
Public Datasets ◽  
Local Image

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

scholarly journals Automatic Detection and Classification of Brinjal Leaf Diseases

2019 ◽  
Vol 8 (11) ◽  
pp. 3647-3650
Keyword(s):  
Feature Extraction ◽  
Wavelet Transforms ◽  
Automatic Detection ◽  
Economic Loss ◽  
Plant Diseases ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Single Feature ◽  
Agricultural Yield

The diseases in the Brinjal can be identified through the symptoms occur in Brinjal leaf. The indication in touch difference bin of various plant diseases. The designation of disease detection need the specialist's opinion. The inappropriate identification can result in tremendous quantity of economic loss for farmers. Rather than manual identification, computers are accustomed to give automatic detection and classifying differing kinds of diseases. In this paper, lesion areas affected by diseases are segmented using different techniques, namely DeltaE, Otsu, FCM, k-means algorithm are employed. The proposed method is the image blend by discrete wavelet transforms to increase the excellence of image and reduce uncertainty and redundancy for identification and assessment of agricultural yield which can be done by DeltaE. Further color, texture and structural based features are mixed collectively for getting better performance when compared with single feature extraction.

scholarly journals Face Recognition Use Local Image Dataset and Correlation Technique

2021 ◽  
Vol 5 (2) ◽  
pp. 26-37
Author(s):  
Dana Faiq Abd
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Correlation Technique ◽  
Final Decision ◽  
Accuracy Rate ◽  
Public Datasets ◽  
Local Image

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

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

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
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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