This work proposes and analyzes a novel methodology for hand-shape image synthesis. The hand-shape is a popular biometric trait with a high convenience of use and non-intrusive acquisition. The proposed algorithm allows to generate realistic images with natural intra-person and inter-person variability. The method is based on the Active Shape Model algorithm which has been modified in order to add the biometric information typical of new synthetic identities. The generated images are evaluated using three public databases and two hand-shape recognition systems. The results show the suitability of the synthetic data for biometric recognition works. In addition, two novel applications have been proposed to provide new insights in hand-shape biometric recognition including: improvement of machine learning classification based on synthetic training sets and scalability analysis of hand-shape biometrics when the population of the database is increased by two orders of magnitude with respect to existing databases.