Machine Learning Approach for Signature Recognition by HARRIS and SURF Features Detector

Mitra, Debasree; Baksi, Aurjyama; Modak, Alivia; Das, Arpita; Das, Ankita

doi:10.26438/ijcse/v7i5.7380

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An architecture using both CNN and Crest Trough method for signature recognition along with Harris and Surf Algorithms for forgery detection is proposed by Jivesh et al [5]. Harris corner detection algorithm and Surf feature extraction algorithm is seen again in the model proposed by Debasree et al in [6].…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

Manikantha¹,

Bhat²,

Nerella³

et al. 2021

JUSST

View full text Add to dashboard Cite

Recognising one’s identity to enter a system is called authentication. This process can take various forms where users input the system with a set of identifying credentials to access the system. Signatures belong to behavioural biometric, where the distinct features of every individual are considered in order to corroborate the person’s identity. The act of falsely imitating one’s signature biometric to impersonate and leverage access to their asset is called signature forgery. Our paper presents a comparative study of various deep learning models using Siamese architecture, over a wide catalogue of signature images. Openly available datasets like CEDAR, Handwritten Signatures dataset from Kaggle, ICDAR 2011 SigComp, and BH-Sig260 signature corpus are used to train the models. A set of classifiers – Support Vector Classifiers (SVC), Gaussian Naïve Bayes (GNB), Logistic Regression (LR) and K-Nearest Neighbours (KNN) are applied sequentially to classify the signature as genuine or forged.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

Manikantha¹,

Bhat²,

Nerella³

et al. 2021

JUSST

View full text Add to dashboard Cite

show abstract

“…It usually constructs rotation-invariant and scale-invariant features based on object shape, texture, and geometric features and collaborates with the general classifiers, e.g., SVM, Bayes, KNN, and other classifiers. Most methods realize object detection by extracting excellent features, such as Harris corner feature [6,7], Histogram of oriented gradient (HOG) [8], scale-invariant feature transform (SIFT) [9,10], and Fourier descriptor [11,12]. These methods have achieved good performance in the field of object detection, but the aircraft scales and shapes in the optical remote sensing images are complex and diverse, and the manual features are difficult to adapt to the diverse remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor

Tang

Wang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft detection methods often use horizontal detectors, which produce significant redundancy, nesting, and overlap of detection areas and negatively affect the detection performance. To address these difficulties, a framework based on RetinaNet that combines a multi-feature fusion module and a rotating anchors generation mechanism is proposed. Firstly, the multi-feature fusion module mainly realizes feature fusion in two ways. One is to extract multi-scale features by the feature pyramid, and the other is to obtain corner features for each layer of feature map, thereby enriching the feature expression of aircraft. Then, we add a rotating anchor generation mechanism in the middle of the framework to realize the arbitrary orientation detection of aircraft. In the last, the framework connects two sub-networks, one for classifying anchor boxes and the other for regressing anchor boxes to ground-truth aircraft boxes. Compared with state-of-the-art methods by conducting comprehensive experiments on a publicly available dataset to validate the proposed method performance of aircraft detection. The detection precision (P) of proposed method achieves 97.06% on the public dataset, which demonstrates the effectiveness of the proposed method.

show abstract

“…A digital image is a two-dimensional array of real numbers that represent visual information. 2-D images are categorized into N-rows and M-columns, integration of these rows and columns is termed as pixels [2]. In the excessive use of the internet and technology, one medium of communication is, through transmitting digital images.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters

Ullah¹,

Kumar²,

Khuhro³

et al. 2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

INTRODUCTION:In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. METHODS, RESULTS AND CONCLUSION:The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels.

show abstract

Machine Learning Approach for Signature Recognition by HARRIS and SURF Features Detector

Cited by 5 publications

References 0 publications

A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor

An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters

Contact Info

Product

Resources

About