Offline handwritten signature verification systems are used to verify the identity of individuals, through recognizing their handwritten signature image as genuine signatures or forgeries. The main tasks of signature verification systems include extracting features from signature images and training a classifier for classification. The challenges of these tasks are twofold. First, genuine signatures and skilled forgeries are highly similar in their appearances, resulting in a small inter-class distance. Second, the instances of skilled forgeries are often unavailable, when signature verification models are being trained. To tackle these problems, this paper proposes a new signature verification method. It is the first model that employs a variational autoencoder (VAE) to extract features directly from signature images. To make the features more discriminative, it improves the traditional VAEs by introducing a new loss function for feature disentangling. In addition, it relies on SVM (Support Vector Machine) for classification according to the extracted features. Extensive experiments are conducted on two public datasets: MCYT-75 and GPDS-synthetic where the proposed method significantly outperformed $13$ representative offline signature verification methods. The achieved improvement in distinctive datasets indicates the robustness and great potential of the developed system in real application.

该研究解决了离线手写签名验证系统中真实签名与熟练伪造签名之间外观相似造成的识别困难，以及训练时缺乏熟练伪造实例的问题。文章提出了一种新型的签名验证方法，首次采用变分自编码器直接从签名图像中提取特征，并通过引入新的损失函数以提高特征的可区分性，使用支持向量机进行分类。实验结果显示，该方法相较于13种代表性方法有显著提升，表明其在实际应用中的强大潜力。

基于特征解耦辅助变分自编码器的离线签名验证