SigmML: Metric Meta-Learning for Writer Independent Offline Signature Verification in the Space of SPD Matrices

Alexios Giazitzis, Elias N. Zois; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 6312-6322

The handwritten signature has been identified as one of the most popular biometric means of human consent and/or presence for transactions held by any number of physical or legal entities. Automated signature verification (ASV), merge popular scientific branches such as computer vision, pattern recognition and/or data-driven machine learning algorithms. Up to now, several metric learning approaches for designing a writer-independent signature verifier, have been developed within a Euclidean framework by means of having their operations closed with respect to real vector spaces. In this work, we propose, for the first time in the ASV literature, the use of a meta-learning framework in the space of the Symmetric Positive Definite (SPD) manifold as a means to learn a pairwise similarity metric for writer-independent ASV. To begin, pairs of handwritten signatures are converted into a multidimensional distance vector with elements corresponding SPD distances between spatial segments of corresponding covariance pairs. We propose a novel meta-learning approach which explores the structure of the input gradients of the SPD manifold by means of a recurrent model, constrained by the geometry of the SPD manifold. The experimental protocols utilize two popular signature datasets of Western and Asian origin in two blind-intra and blind-inter (or cross-lingual) transfer learning approach. It also provide evidence of the discriminating nature of the proposed framework at least when summarized against other State-of-the-Art models, typically realized under a framework of Euclidean, or vector space, nature.