Download free eBook Signature Recognition Using Clustering Techniques
Signature Recognition Using Clustering Techniques. Hemchandra Kekre
Book Details:
Author: Hemchandra KekrePublished Date: 22 Mar 2012
Publisher: LAP Lambert Academic Publishing
Language: English
Book Format: Paperback::180 pages
ISBN10: 384844755X
ISBN13: 9783848447558
File size: 37 Mb
Dimension: 152x 229x 10mm::272g
In this paper we discuss an off-line signature recognition system designed using clustering techniques. These cluster based features are mainly morphological feature, they include Walsh coefficients of pixel distributions, vector quantization based codeword histogram, grid & texture information features and geometric centers of a signature. The signature image is initially pre-processed to facilitate the job of the feature extractor technique. The preprocessing includes the cropping of the signature area, removal of the noise, banalization of coloured image to grayscale one and finally edge detection or smoothing of the signature lines. In this paper we have proposed clustering of these points in vector space to form feature vector is proposed for online signature recognition. For clustering & codebook generation kekre's Vector Quantization Algorithms such as KFCG, KMCG are used with variations. The proposed technique gives up to 97% accuracy. A supervised clustering algorithm for computer intrusion detection Signature recognition learns signature patterns of intrusive (and normal) activities from training data, and then in detection, matches these signatures with the observed technique to obtain the smoothed occurrence frequency distribution of 284 audit- Optic disc is eliminated using Hough transform technique. The candidate exudates are then detected using k-means clustering technique. Finally, the exudates are classified as hard and soft exudates based on their edge energy and threshold. A novel approach for off-line signature recognition system is presented in this work, which is based traditional and soft computing techniques have been used for accomplishing the said task. Due to space constraints, detail discussion of all the techniques are beyond the scope of this paper. Hence, some relevant studies have been shown, below. Handwritten signature recognition using traditional techniques: Author and year Technique(s) Avg. However, a signature can be handled as an image, and hence, it can be recognized using computer vision and artificial neural network techniques. Signature recognition and verification involves two separate but strongly related tasks: one of them is identification of the signature … Signatures are verified depend on parameters extracted from the signature using various image processing techniques. Then Off-line Signature Recognition and Verification is implemented with SURF features and Neural Fuzzy techniques in ANFIS in Matlab. The invented algorithm can be used as an effective signature verification technique. Handwritten Digits Recognition in python using scikit-learn Smart Art Materials & Techniques Recommended Accessibility-How to make your signature Electronic and insert it into a Signature Recognition using Clustering Techniques Clustering techniques – Signature Recognition is using Cluster features along with other feature set Cluster Based Features – 1. Codeword Histogram of a signature template & their Spatial Moments. A Survey: Enhanced Offline Signature Recognition Using Neuro- Fuzzy and SURF Features Techniques Rupali Mehra#, Dr. R.C. Gangwar* #Computer Science Department, Punjab Technical University Punjab,India * Associate professor Because of these advantages, signature recognition has been and can be used in several ways ranging from commercial use to forensic- and government-level applications. For example, logging into enterprise accounts can be done through a mixture of fingerprint and signature recognition biometrics to achieve a better state of security in this space. The signature verification is a typical pattern recognition task. But both types of signatures; online or offline; use different techniques to verify signatures based on either static or dynamic characteristics. The task of signature verification includes extracting some characteristics from the recorded The signature recognition methods are also classified as on-line and off-line, where appropriate dynamic or static features are extracted and analysed. These techniques are well known within the research community [ 7,13,23,24,25,34,41 ]. A new approach to signature recognition using the fuzzy method The signature recognition methods are also classified as on-line and off-line, where appropriate dynamic or static 2 Signature recognition using clustering technique [7] 2.5/8.2 6.5/2.96 95.08 3 Contour Method [35] 11.60 13.20 86.90 Those systems have shown a various degree of success in signature verification.12'3) Other techniques like Fourier transform have also been applied for signature verification with some success/4) Recently, neural network has also been applied for signature verification,tS) In addition to signature verification, signature recognition has also Signature recognition is a behavioural biometric. It can be operated in two different ways: Static: In this mode, users write their signature on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the signature analyzing its shape. This group is also known as "off-line". for the proposed signature recognition task. The feature extraction module has three components. A flow diagram of signature recognition system is presented in Fig. 3. First, SIFT descriptors are extracted from the signature and quantised using the K-means clustering algorithm. Next, the SPM-based scheme is applied for the representation of an Signature Verification is a difficult pattern recognition problem as because no two genuine signatures of a person are precisely the same. Its difficulty also stems from the fact that skilled forgeries follow the genuine pattern unlike fingerprints or irises where fingerprints or … Nowadays forensic document examiners (FDE) have to analyse more and more signatures captured digital devices. While they can still use the static image of the signature, it has been proven that the dynamic information contains very discriminative information. This paper is focused on dynamic signature recognition applied to forensic scenarios. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system. Categories and Subject Descriptors I.4.7 … The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes using a Support Vector Machine. Brain Tumor Detection on MRI Images Using Segmentation and Clustering Full Matlab Project Code. .we present a system based on gabor filter based enhancement technique and feature extraction techniques using texture based segmentation and SOM (Self Organization Map) which is a form of Artificial Neural Network (ANN) used to analyze the (PDF) Signature Recognition using Cluster Based Global Features | Vinayak Bharadi - In this paper we discuss an off-line signature recognition system designed using clustering techniques. These cluster based features are mainly morphological feature, they include Walsh coefficients of pixel distributions, vector quantization based presents a design of offline Signature recognition system using neural networks.A database is created applying global and morphological operations on the signature using different ranges. Database of total 1344 signatures of 7 persons are used for experimentation.Daubechies wavelet Srikanta Pal, Michael Blumenstein and Umapada Pal. Automatic off-Line Signature Verification Systems: A Review. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (14):20-27, 2011. Full text available.
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