用变分自编码器+特征解耦提升手写签名验证准确率
Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder
- 基于改进的VAE直接从签名图像提取特征
- 在两个数据集上优于13种现有方法
- 适合需要高精度签名验证的场景
离线手写签名验证系统通过识别手写签名图像来验证个人身份，主要任务是从签名图像中提取特征并训练分类器进行分类。该任务面临双重挑战：真实签名与高仿真伪造签名外观高度相似，导致类间距离小；且训练时往往缺乏高质量伪造样本。为此，本文提出一种新方法，首次使用变分自编码器（VAE）直接从签名图像中提取特征，并通过引入新的特征解耦损失函数改进传统VAE，使特征更具判别性。随后利用支持向量机（SVM）对提取特征进行分类。在两个公开数据集MCYT-75和GPDS-synthetic上进行的大量实验表明，所提方法显著优于13种代表性离线签名验证方法，结果证明了系统的鲁棒性和实际应用潜力。
原文摘要 · Abstract (English)
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.
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