Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-line Chinese signature verification based on support vector machines
Pattern Recognition Letters
A fast learning algorithm for deep belief nets
Neural Computation
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Journal of Cognitive Neuroscience
Exploring Strategies for Training Deep Neural Networks
The Journal of Machine Learning Research
Justifying and generalizing contrastive divergence
Neural Computation
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
IEEE Computational Intelligence Magazine
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. This task is a difficult vision problem within the field of biometrics because a signature may change depending on psychological factors of the individual. Motivated by advances in brain science which describe how objects are represented in the visual cortex, advanced research on deep neural networks has been shown to work reliably on large image data sets. In this paper, we present a deep learning model for off-line handwritten signature recognition which is able to extract high-level representations. We also propose a two-step hybrid model for signature identification and verification improving the misclassification rate in the well-known GPDS database.