Abstract
The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmentedr-continuous bits matching rule andP-receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification.
Highlights
Handwriting signature recognition is an effective identity authentication method by using signature data, since every person’s signature is different, and especially the dynamic characteristic is difficult to imitate
We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification
We present a novel clonal selection algorithm for synthetic sample generation
Summary
Handwriting signature recognition is an effective identity authentication method by using signature data, since every person’s signature is different, and especially the dynamic characteristic is difficult to imitate. An important challenge is that most existing approaches require sufficient signature samples to guarantee the effect. The performance evaluation of these systems needs to provide a large number of test samples [4]. Most of the classifier algorithms’ (such as neural networks, hidden Markov model) performance generally depends on the amount of training data, and training a stable and efficient classifier needs providing a sufficient number of samples [5]. Some commercial signature databases have been established, the sharing and distribution of these data are very difficult due to some legal issues [6]. The number of signature databases that can be shared is fairly limited. The database collection is time consuming and expensive, since users are unwilling to submit their privacy data due to potential security problems. The boring repeated submission process will affect the quality of signature samples
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