Low-shot palmprint recognition based on meta-siamese network

Palmprint is one of the discriminant biometrical features of humans. Recognizing palmprints in complex environments is a significant multimedia task, which is highly suitable for applications in information security and forensics. Recently, deep learning-based recognition methods have improved the accuracy and robustness of recognition results to a new level. However, obtaining the required large amount of training data and labels is impracticable in practical scenarios. Therefore, in this paper, we exploit few-shot learning for palmprint recognition. We propose Meta-Siamese network based on Siamese network. Specifically, we train this network episodically with a more flexible framework to learn both the feature embedding and the deep similarity metric function. Moreover, we extend our model to zero-shot recognition tasks based on deep hashing network. Experiment result shows competitive improvements compared to baseline methods in eight different datasets.