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An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
- Source :
- ICPR
- Publication Year :
- 2020
-
Abstract
- SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.<br />arXiv admin note: text overlap with arXiv:2004.03373
- Subjects :
- FOS: Computer and information sciences
021110 strategic, defence & security studies
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
0211 other engineering and technologies
Computer Science - Computer Vision and Pattern Recognition
Pattern recognition
Feature selection
Context (language use)
02 engineering and technology
Overfitting
Convolutional neural network
Feature (computer vision)
Handwriting recognition
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Transfer of learning
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICPR
- Accession number :
- edsair.doi.dedup.....0fcd9c356648413c2e545a046ee884d7