On-line Handwritten Signature Verification using Machine Learning Techniques with a Deep Learning Approach
(2015) In Master's Theses in Mathematical Sciences FMA820 20151Mathematics (Faculty of Engineering)
- Abstract
- The problem to be solved in this project is to distinguish two signatures from each other, with help of machine learning techniques. The main technique used is the comparison between two signatures and classifying if they are written by the same person (match) or not (no-match). The binary classication problem is then tackled with a few alternatives to better understand it. First by a simple engineered feature, then by the machine learning techniques as logistic regression, multi-layer perceptron and nally a deep learning approach with a convolutional neural network.
The evaluation method for the dierent algorithms was a plot of true positive rate (sensitivity) versus false positive rate (fall-out). The results of the alternative... (More) - The problem to be solved in this project is to distinguish two signatures from each other, with help of machine learning techniques. The main technique used is the comparison between two signatures and classifying if they are written by the same person (match) or not (no-match). The binary classication problem is then tackled with a few alternatives to better understand it. First by a simple engineered feature, then by the machine learning techniques as logistic regression, multi-layer perceptron and nally a deep learning approach with a convolutional neural network.
The evaluation method for the dierent algorithms was a plot of true positive rate (sensitivity) versus false positive rate (fall-out). The results of the alternative algorithms gave a dierent understanding of the problem. The engineered feature performed unexpectedly well. The logistic regression and multi-layer perceptron performed similarly. The main results from the nal model, which was a max-pooling, convolutional neural network, were a true positive rate of 96.7 % and a false positive rate of 0.6 %.
The deep learning approach on the signature verication problem shows
promising results but there is still room for improvement. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8055778
- author
- Drott, Beatrice ^{LU} and Hassan-Reza, Thomas ^{LU}
- supervisor
- organization
- course
- FMA820 20151
- year
- 2015
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Deep Learning, Convolutional Neural Networks, Signature Verification, On-line Handwritten Signatures, CNN, Biometric Recognition, Machine Learning, Supervised Learning, Logistic Regression, Multi-layer Perceptron, Artifical Neural Networks
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3282-2015
- ISSN
- 1404-6342
- other publication id
- 2015:E40
- language
- English
- id
- 8055778
- date added to LUP
- 2015-11-11 13:17:00
- date last changed
- 2015-11-11 13:17:00
@misc{8055778, abstract = {The problem to be solved in this project is to distinguish two signatures from each other, with help of machine learning techniques. The main technique used is the comparison between two signatures and classifying if they are written by the same person (match) or not (no-match). The binary classication problem is then tackled with a few alternatives to better understand it. First by a simple engineered feature, then by the machine learning techniques as logistic regression, multi-layer perceptron and nally a deep learning approach with a convolutional neural network. The evaluation method for the dierent algorithms was a plot of true positive rate (sensitivity) versus false positive rate (fall-out). The results of the alternative algorithms gave a dierent understanding of the problem. The engineered feature performed unexpectedly well. The logistic regression and multi-layer perceptron performed similarly. The main results from the nal model, which was a max-pooling, convolutional neural network, were a true positive rate of 96.7 % and a false positive rate of 0.6 %. The deep learning approach on the signature verication problem shows promising results but there is still room for improvement.}, author = {Drott, Beatrice and Hassan-Reza, Thomas}, issn = {1404-6342}, keyword = {Deep Learning,Convolutional Neural Networks,Signature Verification,On-line Handwritten Signatures,CNN,Biometric Recognition,Machine Learning,Supervised Learning,Logistic Regression,Multi-layer Perceptron,Artifical Neural Networks}, language = {eng}, note = {Student Paper}, series = {Master's Theses in Mathematical Sciences}, title = {On-line Handwritten Signature Verification using Machine Learning Techniques with a Deep Learning Approach}, year = {2015}, }