Abstract
Handwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated analysis method for the dynamic electronic representation of handwritten signature authentication was researched. The developed algorithms are based on the dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the designed electronic pen described in the paper. The triplet loss method was used to train a neural network suitable for writer-invariant signature verification. For each signature, the same neural network calculates a fixed-length latent space representation. The hand-corrected dataset containing 10,622 signatures was used in order to train and evaluate the proposed neural network. After learning, the network was tested and evaluated based on a comparison with the results found in the literature. The use of the triplet loss algorithm to teach the neural network to generate embeddings has proven to give good results in aggregating similar signatures and separating them from signatures representing different people.
Highlights
The development of biometric verification methods is one of the significant trends in current scientific research
We presented an efficient user verification approach based on a dynamic signature using a specialized device designed for this task
Our method utilizes the triplet loss algorithm to train a neural network model that can be used to extract meaningful features from signatures, in the form of fixed-length embeddings that group well for signatures of a single person and separate from other signature groups pertaining to other clients
Summary
The development of biometric verification methods is one of the significant trends in current scientific research. There are many approaches to the classification of a handwritten text, i.e., signaturebased person authentication [11] Examples of such methods are dynamic time warping, Gaussian mixture models, fuzzy modeling, and many others. We developed an algorithm based on a convolutional neural network trained with the triplet loss method. This network is utilized to analyze dynamic features obtained from the biometric pen’s sensors, as well as static features (i.e., the shape of the signature), and output a fixed-length representation that could be used to compare and group together signatures. Some of which we discuss briefly, rely on the computation of signatures’ representations using a deep neural network and involve performing the comparison using an additional neural network [20,21].
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