Off-Line Handwritten Chinese Signature Verification Based on Support Vector Machine Multiple Classifiers

Abstract Inlet start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection controls of scramjets. In ground and flight tests, it is inevitably to introduce the sensor noises to the measurement system. How to overcome or weaken the influence of the sensor noises and the outer disturbances is an important issue to the control system of the engine. To solve this problem, the 2D inner steady flow of hypersonic inlets was numerically simulated in different freestream conditions and backpressures, and two different inlet unstart phenomena were analysed. The membership function for hypersonic inlet start/unstart can be obtained by using probabilistic output support vector machine, and the algorithm of multiple classifiers fusion is introduced. The variations of the classification accuracy with the intensity of the sensor noises and the number of the classifier were discussed respectively. In conclusion, it is useful to introduce the algorithm of support vector machine and multiple classifiers fusion to overcome or weaken the influence of the sensor noises on the classification accuracy of hypersonic inlet start/unstart. The number of the practical fusion classifiers needs a tradeoff between the fusion classification accuracy and the complexity of the classification system.

Download Full-text

Study on the Self-Organize Selective Fusion Support Vector Machine Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.282-283.165 ◽

2011 ◽

Vol 282-283 ◽

pp. 165-168

Author(s):

Yong Ming Cai ◽

Qing Chang

Keyword(s):

Support Vector Machine ◽

Classification Performance ◽

Small Sample ◽

Support Vector ◽

Support Vector Machine Algorithm ◽

Multiple Classifiers ◽

Memory Overhead ◽

Selective Fusion ◽

Statistical Learning Method ◽

Sample Support

As a major statistical learning method in case of small sample, Support Vector Machine Algorithm (SVM) has some disadvantages in dealing with vast amounts of data, such as the memory overhead and slow training. we use Multi-class Support Vector Machine (MSVM) with Self-Organize Selective Fusion (SOSF) to optimize the multiple classifiers selectively, which can update the classification and self-adjust its classification performance, and eliminate some redundancy and conflicts, achieve the fusion of multiple classifiers selectively, and effectively solve the shortcoming of disturbances by the sub-samples distribution in large sample, and improve the training efficiency and classification efficiency.

Download Full-text

Fault Diagnosis for Aircraft Engine Based on SVM Multiple Classifiers Fusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.607 ◽

2013 ◽

Vol 433-435 ◽

pp. 607-611

Author(s):

Feng Tian ◽

Wen Jie Li ◽

Zhi Gang Feng ◽

Rui Zhang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Recognition Rate ◽

Aircraft Engine ◽

Support Vector ◽

Feature Vectors ◽

Multiple Classifiers ◽

The Neural Network ◽

Multiple Classifier ◽

Multiple Classifiers Fusion

Support vector machine (SVM) could well solve the over-learning and the low generalization ability of the neural network. But the single classifier cannot achieve satisfactory recognition rate and anti-interference ability. An aircraft engine fault diagnosis method based on support vector machine multiple classifiers is proposed in this paper. Firstly, sample characteristic information which constitutes the fault feature vectors obtained from the existing engine fault. Then, after training the SVM multiple classifier by faulty feature vectors, the SVM model of the fault diagnosis system is established; Finally, the trained SVM multiple classifier is used to recognize and classify the test faults. Applying the noise on the test samples, SVM multiple classifiers can still get a good diagnosis effect. It shows that the fault diagnosis algorithm has good robustness and can be applied to the study of aero engine fault diagnosis.

Download Full-text

Signature Verification Using Support Vector Machine and Convolution Neural Network

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i1s.1564 ◽

2021 ◽

Vol 12 (1S) ◽

pp. 80-89

Author(s):

Kritika Vohra, Et. al.

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Extraction ◽

Aspect Ratio ◽

Convex Hull ◽

Unique Feature ◽

Signature Verification ◽

Support Vector ◽

Individual Signature ◽

Business Functions

Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.

Download Full-text

On line signature verification: Fusion of a Hidden Markov Model and a neural network via a support vector machine

Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition ◽

10.1109/iwfhr.2002.1030918 ◽

2003 ◽

Cited By ~ 30

Author(s):

M. Fuentes ◽

S. Garcia-Salicetti ◽

B. Dorizzi

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Signature Verification ◽

Support Vector ◽

On Line

Download Full-text

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

International Journal of Multimedia Data Engineering and Management ◽

10.4018/ijmdem.2017040103 ◽

2017 ◽

Vol 8 (2) ◽

pp. 22-32 ◽

Cited By ~ 3

Author(s):

Subhash Chandra ◽

Sushila Maheshkar

Keyword(s):

Support Vector Machine ◽

Texture Features ◽

Signature Verification ◽

Support Vector ◽

Gray Level ◽

Global Level ◽

First Order ◽

Svm Model ◽

Level Information ◽

Occurrence Matrix

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

Download Full-text